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Leonard Cohen interviews Suzanne Vega in 1993

Part 1

Leonard Cohen: Alone at last. This is the first time we’ve ever been alone in a room together.

Suzanne Vega: Is that really true? Yeah, I guess it is true.

C: I met you first at the photographers.

S: Right, so it was you and me and that woman.

C: And assistants.

S: … and assistants …

C: … and well wishers, and onlookers.

S: Right. Then we went for a drink.

C: But, that was a public place.

S: … that was a public place.

C: And what was the next time we met?

S: At the Juno Awards.

C: You were very kind to come to the Juno Awards and sing a fragment of my song.

S: I was very happy to come and sing a fragment of your song.

C: That was very, very kind of you. But, you could hardly say that was a private occasion.

S: Right. No, it wasn’t a private occasion.

C: Our circumstances were different also. Your life has changed radically since then.

S: Well, I guess it has. I suspect yours has, although I don’t know how, you know. Oh, you know because I wrote to you and told you, that’s why.

C: Yes. I consider the letter very sweet, and I was touched by the fact that you would inform me about your situation. May I apologize for not responding?

S: Oh, no problem.

C: I myself have been in the midst of a creative struggle of some dimension. But, our lives have changed radically since that last hurried meeting in many crowded places in Vancouver.

S: Yes, I guess it was Vancouver.

C: Now we find ourselves…

S: But I did receive your Christmas present.

C: Oh good, what was it?

S: It was the dates that you sent through the mail.

C: I’m so glad that you got those. Did you not get a little gift from me after the Juno?

S: Of course I did and I thanked you for it too.

C: Oh, good. I’m glad you thanked me for it. And did you like the dates?

S: Yeah, I liked the dates. I liked them the year before that too.

C: I hope I’ll be able to send you a box of dates every year at Christmas, until circumstances really change and that extravagance can no longer be supported.

S: So, about your new album… no, I was kidding.

C: I’ve been reading the lyrics of your new album, which your management has kindly furnished me. I’ve been listening to the album under many circumstances. I’ve been listening to it in the car, with the sunroof open and closed. It sounds very, very different. The bass disappears when you open the sunroof.

S: Oh, really?

C: …in the sound system in my car, no flaw in your record. In fact, if I may say so, the low notes, not that you sing but that are played…

S: …yes…

C: …are very, very beautiful.

S: and low, and…

C: There’s a wonderful lowness. There’s a wonderful lowness about your record.

S: I feel this too, actually. We got very low to do this record.

C: I don’t think you ever had anything quite this low in your previous work, the sounds … bass sounds…

S: It’s the bass sounds, but also in the attitude I think in keeping what might normally be discarded. Keeping all of it, the distorted parts and the noise that someone else might throw away. We kept it.

C: Why?

S: There’s a low sort of attitude.

C: Were you feeling reckless?

S: Yeah, I suppose I was feeling rather reckless. I was feeling like… I mean, I have no complaints with any of my previous records, but I felt that it was all very clean and some of the things I write about are not especially clean. Art is kind of low and dark and so I felt that it was time to incorporate some of that into the music. I think that’s probably why I picked Mitchell Froom to make the album. He has a taste for those kind of things.

C: It’s very, very successful, and your voice against some of those moments are very beautiful, very pure. But are you as pure in your own life, in your own views as the singer presents herself? There’s an austerity and a kind of unstrident idealism about the record, that is if somebody has kept something, kept some flame alight, kept something unsullied. That’s a feeling that runs through the record. Is that so in your life? Do you lead a life that is guarded?

S: I might say I lead a guarded life, yeah. I mean it’s not as pure as it might look, but it’s not… it’s pretty guarded. I think it’s guarded because it’s had to have been guarded. It’s because I came from places that were not very pure and I suppose that’s why I felt I needed to keep certain things clear and straight. But still I feel myself to be of the world and looking at things that are real and things that are not pure. I don’t feel that I judge other people, but I judge myself very strictly.

C: You are a strict person, and other too I imagine you judge quite strictly. Except the people you happen to fall in love with. Then I imagine you make, as you say in one of your songs, in your song In My Movie …

S: If You Were In My Movie

C: If You Were In My Movie , in that song you seem to indicate that you would give wide allowance to anyone you fall in love with.

S: I don’t know if that’s true, maybe, probably. I don’t know.

C: Is that what the song is about?

S: The song is about flirting. It’s a flirting kind of song. It’s a song looking at another person and saying these are qualities that you could be, that you could have within you. These are the things that I see.

C: You could realize these things with me.

S: Yeah, if you wanted to. It’s putting a glamorous light on someone’s character. Saying these are the things that, when I look at you, these are the things I see. It’s like taking someone’s basic nature and making it more than it actually is.

C: You have managed to make austerity extremely seductive. There is a very seductive quality about your record, although nothing is given away, nothing is thrown away, nothing is revealed.

S: Except in the art work where you can see my legs.

C: I haven’t seen, … nobody showed me any artwork.

S: Well, I’ll have to show… when we’re done with the interview, I’ll show you the artwork. But, in that one I’m dressed as on of the characters on the album, the character of the dancing girl, and so, I’m wearing a dancing girl outfit and you can see my legs. But, I’m still wearing men’s shoes and I’m wearing a cardigan sweater. So, I suppose you’re right because there isn’t anything being revealed, although it’s hinted at.

C: It’s into,… I mean there is nothing in the record that rejects anything that is going on in this world. It casts quite a cold eye on the things around you, but there is a flirtatious, …no flirtatious, I wouldn’t use that word. There’s a very seductive quality to all your attitudes, especially the most restrained of them. I think that’s the genius of the album, behind the very careful construction of the songs and the very sparse lyrics there is some kind of raging appetite. I heard you once on the Howard Stern Show.

S: Oh, you did?

C: Yes, it was one morning a few months ago.

S: Oh really. Do you happen to remember what he was discussing specifically?

C: Well, I think he was discussing your breasts.

S: Yeah, I remember that day.

C: And, he seemed to be pleased.

S: He did seem to be pleased, I remember that.

C: He seemed to be pleased for one reason or another.

S: Right.

C: But the thing that pleased me about the interview, of course next to the astounding information that Howard Stern imparted to us all…

S: Right.

C: … About your anatomy, was just the sound of your speaking voice…

C: … were you surprised at the fact that many people love you?

S: I’m surprised at it, yeah, if you put it like that. It makes me feel shy.

C: Did you not expect to be loved so widely?

S: I don’t think I expected to be understood. Whether I expected to be loved…

C: I don’t understand you. What kind of understanding have you…

S: I’m surprised when people understand as much as they do of the songs, because I guess I don’t reveal a lot about the specific topics. You know when people say, “Well what’s your message?” I never feel that I’m just revealing a message. I guess I felt if I was going to do that I could write out a message on a pamphlet or something and pass it around, and that would be a message of a kind, but it doesn’t seem to be the way to do it that makes the most sense to me.

C: Well, I think that you are revealing something. There’s something in the most refined and abstracted way flirtatious about the way that you refuse to reveal anything.

S: Well, I think it’s because the things that attract me in real life are the things that are not obvious and the things that are not simple.

C: But do you have a kind of passion for this thing that cannot be said? May I ask you to read, would you mind reading?

S: No, I wouldn’t mind.

C: For instance, the lyric of the “…Dancing Girl”

S: Okay. This song is called Fat Man and Dancing Girl and it goes:

I stand in a wide flat land
No shadow or shade of a doubt where the megaphone man
met the girl with her hand that’s
covering most of her mouth

Fall in love with a bright idea and the way a world is revealed to you
Fat man and dancing girl
and most of the show is concealed from view

Monkey in the middle keeps singing that tune
I don’t want to hear it
Get rid of it soon

MC on the stage tonight
Is a man named Billy Purl
He’s The International Fun Boy
And he knows the worth of beautiful girl

Stand on the tightrope
Never dreamed I would fall

Monkey in the middle
Keeps doing that trick
It’s making me nervous
Get rid of it quick

I stand in a wide flat land
No shadow or shade of a doubt
Where the megaphone man
Met the girl with her hand
That’s covering most of her mouth

Does she tell the truth?
Does she hide the lie?
Does she say it so no one can know?
Fat man and the dancing girl
And it’s all part of the show

Stand on the tightrope
Never dreamed I could fall

Monkey in the middle
Keeps singing that tune
I don’t want to hear it
Get rid of it soon

Monkey in the middle
Keeps doing that trick
It’s making me nervous
Get rid of it quick

C: Oh, I thank you so much for reading that. I think that it has, … I think that we should study it, a little.

S: Oh yeah?

C: …carefully. I did study this song with my son, and we went through the lyric line by line.

S: Your son, Adam?

C: My son, Adam. I would love to have the opportunity to study it with you. Because, well for one thing, I think it’s very, very beautiful, beautifully executed song on the album. I think that there are lines in it that get right to the heart of your operating mode, and I’d really like to see what I could uncover for myself and for the listener. So, let’s begin at the beginning and please forgive me if I question you in what seems to be insane detail.

S: Well, I might just say, “Well, I just can’t tell you that”.

C: That’s fine. I think that our friendship will survive this examination.

S: Okay.

C: The first line that I really would like to ask you about is this line, “and most of the show is concealed from view.” What is the show that is concealed from view?

S: The way I was thinking of it was almost like a shadow puppet; the thing that is really causing the shadow is the thing that’s behind the screen. But, that’s not really answering your question. “Most of the show is concealed from view,” meaning the real life no one sees. It’s the thing that happens when I go home, or when I think about my own life or when I think about other people’s lives. The thing that is the most interesting about people is the way they are when no one is looking at them or the way they are when they’re in private.

C: Well, what do you see in this world?

S: And to me that is the kind of show that I give. I don’t give a glamorous show. I don’t come on stage in costumes or outfits.

C: Oh, I see what you mean.

S: Although, in this particular song, I’m playing at being the dancing girl. But, when I say, “most of the show is concealed from view,” the real heart of the whole show is the thing that I don’t do on stage. It’s the private part.

C: So the resonance in your voice, the activity that your lyrics point at is the real song?

S: Yes.

C: And it’s a kind of brush painting, where a line or two will indicate a horizon, or a sky, or a sea, or a mountain, and it’s just done with one or two strokes. I accept that as a partial explanation, but it’s too insistent this record, the lyric is too insistent. Song after song you seem to indicate there’s something going on behind the curtain in “As Girls Go.” You say that if you could just run that number yourself and you could see behind the other side of the curtain you’d understand the situation. But, there is something that is whispering to you and something whispering to the listener all through the record. You don’t have to hear it this way, you could just tap your foot to the record. It’s a great record. But, for those of us that like to torture ourselves in other realms,…

S: Yes.

C: … and those of use who are compelled to do interviews with imperial intentions in the middle of an afternoon these are the thoughts that assail us, there is something whispering to you, and it’s something menacing. It’s something…

S: …dark

C: …something fertile, it’s something wet. It’s something sexual, it’s something violent. What is it really?

S: Well, it’s different things in each of the songs. It’s different things in each of the songs, in the place that you mentioned about what goes on behind the curtain. In that song it’s wondering how far did this person take their own wish to be somebody else. You know, that’s a song about a woman, by all appearances she’s a woman except that you know she’s a man. So you see someone like this who seems very rare. This one particular person had a very rare quality which you could kind of understand after you realized what her situation was. But, it didn’t explain everything. It just made her extremely attractive and so you felt yourself drawn into her because of this rare quality and then you start to wonder how far did the whole thing go. How much pain does this person put themselves through in order to present this extremely attractive appearance, this extremely graceful and beautiful appearance. So that was my question. I mean, I never found out the answer. I didn’t need to know the answer. It was more just the way this person was alluring.

C: How much pain do you go through to present this extremely attractive, modest and refined appearance?

S: I think I’ve experienced a fair amount of pain in my life, but I don’t feel that that’s a part of the show really.

C: You have a clear idea of what the show is?

S: Yeah, I know myself pretty well. I know what my own history as been. But, I don’t feel that I need to,.. you know I take parts of it and make things out of it. And mix it with other things that I know and things that I see. How much pain do I put myself through? I don’t know. I mean, I have to say that at this point in my life I’m happier than I’ve ever been,

C: How come?

S: Cause I feel really free, I think, for the first time in my whole life. I think I feel very much like myself and not concerned with proving something to someone or… I feel like some of those more idiosyncratic parts are starting to come out now in a way that I would not have allowed before.

C: You have money, fame, youth, beauty, talent. That’s a good start… for feeling good.

S: Yeah, but you know that doesn’t mean that people are happy if they have those things. I stick to my original theory.

C: Which is?

S: Which is that I feel very free right now. I feel very happy with myself, with my own character as it is. And those other things are good, and … I’m not working a day job. I’m really happy about that. But I don’t feel that it’s these other things that have made me feel the way I’ve been feeling.

C: Do you have many admirers?

S: I have some.

C: I image they are legion. Would you please tell me what this means, “monkey in the middle keeps singing that tune, I don’t want to hear it, get rid of it soon.”

S: Well, the “monkey in the middle,” – first of all, in order to describe a song like this you have to describe the landscape it’s taking place in.

C: Well, we have all the time in the world.

S: Okay. The “wide flat land” is obviously not a real land. It’s a land in someone’s mind or it’s a land you might see in one of Picasso’s paintings. You know, like the Harlequin series. It’s a circus atmosphere, but it’s like a bad dream or like a nightmare.

C: It’s too real?

S: Too real? No, I said surreal. So in this landscape you have, … what “monkey in the middle’ meant to me was that there was a person in my life who was telling me something over and over again that I didn’t want to hear. I kept trying to get rid of the thing this person was saying, cause I felt this person wasn’t understanding.

C: A real person in your life you mean?

S: Yeah, it was a real person in my life. But, within this landscape she became the monkey in the middle and I kept trying to get rid of it…

C: She’s a voice in your mind and she belongs to a real person and the things she said disturbed you deeply or inhibited you or prevented you from acting freely?

S: The thing that this woman said was.. she was warning me of something, to be careful of something. I didn’t feel like being careful, and in the end she was right and I was wrong. The monkey, the tune was the one I finally heard.

C: That’s a warning voice.

S: Yeah, yeah.

C: And you found that her warnings were well…

S: Accurate.

C: … well conceived.

 

Part 2

S: Yeah, well conceived. Cause there are certain areas where I’m not cautious, where I just go tumbling headfirst and I think sometimes, in this case her advice was, yeah, well conceived. But, each of these characters is someone in my life ans I wouldn’t feel comfortable telling you who the different people are.

C: No, no, I know.

S: But, there’s a function to each one. The megaphone man is the opposite of the girl with the hand over her mouth. The megaphone man is a person who gives information to the world. The girl who is covering her mouth is the girl with the secret, it’s the same girl that’s in all the songs. It’s the same girl…

C: She has a secret?

S: Yeah.

C: It’s a delicious secret sometimes.

S: Could be.

C: Or a dark secret.

S: It’s a dark one. It’s probably no different than the same secret every woman has. Based on that…

C: What is the secret that everywoman has?

S: Well, I’m sure yo uwould know.

C: I don’t.

S: I’m sure you’ve experienced it several times, over and over again in your life. It’s probably nothing more or less than that, except that sometimes it’s dark, sometimes it’s violent, sometimes it’s stuff that you knew too early that you shouldn’t have known.

C: That’s another theme in this record, or at least in one of the songs, two of the songs, that there is something you find out too early. Now I don’t mean to be tedious with this emphasis on this secrecy bu tnot everybody writes every song about something that happens offstage, about something that is concealed, about a secret that is not told, not whispered.

S: Do you think every song is about this?

C: It appears in a number…

S: In a number.

C: … of the songs. It’s a strong theme in the record, and that’s why I’m just poking around trying to find out what this is. Not what the secret is but what your devotion to the secret is and how it became in a certain sense the aesthetic irritation around which the pearl of the song formed. It’s something that seems to be very present in your psyche, this notion that there’s something to be concealed, something to be discovered, something not quite heard, something not quite understood, sonething glimpsed behind teh veil. It seems to be there over and over again and forgive me for trying to uncover something which has been so deliberately concealed.

S: Well, I understand your reasons for it, but I suppose in the long run, It’s become the way I prefer to work because there’s something beautiful in it to me. There’s something beautiful in presenting it that way with the whole mystery about it intact. I think the kind of writing that I always loved was the kind of writing that had all the complications in it and everything was not explained completely. You have to say the same thing about your own work. You don’t reveal everything, relationships are not always clear. There’s a lot of specific things that are hinted at and you fill in the rest with your imagination but you don’t come out and blurt out the sort of obvious arithmetic of it. You don’t come out and say, “Well, I loved you and you don’t love me,” although maybe you have said that.

C: I say it over and over again, I thought. Incidentally, there is very little…

S: Which is why I was attracted to your music at a very early age, it explained, to me it had the world the way I knew it. It didn’t try and make it simple, it didn’t try and clear it up for everyone. It kept it as murky as it actually is in life, and that to me is what I like about it.

C: What did you learn too early?

S: I learned about the way people can treat each other and the way people, in extreme circumstances, will do things that they wouldn’t do if they were thinking about it; how people, at a very basic level, wlil fight to survive and act in ways that humans would prefer to not think of themselves acting like. Those are things I think I learned pretty early. That was my sense of the world, as a place where… the world I grew up in was a very extreme place, it seemed to me. MAybe it was just because of my temperament. I don’t think so. I don’t think it was because of my temperament. I think it was the circumstances. those were the first things I think I learned. I mean, I learned other things as well, but those were the things I learned too early. The other things I learned were things that children know, which are things of the imagination and things, you know, more spirit-like things. Things like, myth-like things. Those are things I also knew as a child.

C: What is the mystery of that poem? Would you mind reading it? I think it’s a wonderful song. I listen to your songs in the car, with the sunroof open and closed, and listening to it in a room, and listening to it in the bath, and I find it has the quiality of allowing you to leave the song and go off into your own considerations, of your own predicament where it becomes a kind of score, a kind of background for your own speculations. And I found myself, after I allowed myself to relax with the record beyond all the implications and obligations of the interview that I knew I would have to do, I tried to expose myself to the record in the normal fashion and I found that you could drift away a lot of the time, which I think is the test for music that I like. You’re very polite in this record.

S: Am I?

C: In fact, you are a very polite person.

S: I think I probably am a polite person. It’s gotten me in trouble many times.

C: Really?

S: Yeah.

C: that’s a curious world in which courtesy and good-manners now gets people in trouble.

S: Well, it did. For example, you have to imagine, say, if someone is in the ocean and they’re drowning, it would be very bad. This is something that actually happened to me when I must ahve been twelve or thirteen, and I felt myself suddenly way over my head, and I found myself saying, “excuse me please, but do you think you could come over here and take me out of this water because I think I’m drowning.” And you know you say it in this perfectly reasonable, …

C: Instead of screaming out, “Help!”

S: … instead of going, “Help!”, yeah. It’s the kind of thing… that’s the kind of thing I mean. I would either prefer to swim to a shallower place by myself or somehow ploitely engage someone else in this life-to-life activity.

C: You thought that help would violate this sacred space between people that must at all times be preserved, this secrecy, this restraint.

S: I don’t knwo what it was, I just felt really foolish. By the time I got out my fingernails were purple. I thought, “Well that was really stupid.” I thought to myself, “Why didn’t you just say ‘help?’ Why didn’t you just shout?” And go “Help!” And it is a polite record, and it is a strange way of threatening someone, this song here. To say “excuse me, if I may, turn your attention my way” is a terribly polite way of saying I’m going to kill you with this rock.

C: That’s right.

S: So, I think you’re right.

C: Who taught you these manners? Did you acquire them yourself?

S: I have no idea, my mother says I was just always like that. She says I carried myself with this way of being like a princess and even if I was going through the garbage it was always with a certain manner, which I think sometimes other people found annoying. It wasn’t the kind of thing that I was taught. I have no idea where I got it from.

C: this was a style you acquired very early in your life, this kind of strong sense of the importance of maintaining the appropriate tine and distance between you and the world, and you and other people.

S: It was before I was seven years old, I’d say.

C: Not to say that you reject anything, but that you have a very well spun filter between you and the phenomena that surrounds you, for which we must be grateful because it produces these extremely mysterious and interesting songs. It is true that someone you’re going to thump on the head with a rock, even if it’s a small rock.

S: A small rock.

C: Is this some idea of the David story?

S: Yeah, it’s smoe idea of it. It’s a very simple version of the story of David and Goliath. It’s the moment where he’s trying to get Goliath’s attention, you might say. Maybe Goliath in his mind is saying, well you’re too small. You’re just too small for me, I can’t even look at you because you’re too small. David is saying, well, it’s this small thing that can bring you down, that will cause your fall.

C: The power of the small.

S: Yeah, the power of the small thing.

C: You’ve mastered that, the power of the small.

S: At some point I hope to grow, it’s the thing I’m very interested in. It is one of the things I’m very interested in.

C: Which is?

S: That power of te small. That idea that small things have their own voice and their own will and their own life and their own dignity in the world. That is very often trampled on by people who feel they are bigger.

C: You know I was asking myself what is the essential quality of the record and that wass the word that came to me was dignity, that it’s dignified, that all your work is very dignified. That it doesn’t surrender to vulgarity, that it never panders. Dignity is the quality that no matter what you’re talking about you never surrender that. You never turn it into a peek show, even though you’re completely concerned with this notion of curtains and concealment and what is wehispered in secret, you never become coy about it. And I think that’s a significant achievement of your work is the dignity that it never surrenders even while talking about matters could easily fall into an undignified confessional mode. It never even approaches that. Is this a man you’re speaking to?

S: In this song? In the “Song of David?”

C: Or is it the world?

S: It’s not a specific man. Sometimes I feel like it’s the world. Sometimes I feel it’s the way I approach the world or the audience even, I stand on the stage and I say, “Excuse me, if I may.” That’s the thing I want. I want their attention for that moment and somehow by the end of the show I will have made them see something. So sometimes I feel that it’s my way of approaching the world or the audience, sometimes it’s a way of approaching someone I feel to be bigger than myself. And it’s not usually a man that I’m involved with but someone that I perceive as having authority. It’s a song about authority. It’s a song about striving to get that authority to know you, to know a person.

C: Is this in anyway a song about your life or your career, which all of us write in some kind of secret way, those songs where you say, “look you’ve underestimated me?” If you want to relegate me as a folk singer or as this particular kind of performer or this particular kind of writer, you’ve got the wrong idea.

S: Yeah, there’s an element of that. There’s an element of that kind of challenge. Definitely.

C: So, a lot of the reviewers that I’ve read have made some point that this record has its flirtatious element, or that you’ve changed, that this represents a radical change in your work or in your direction, is it so?

S: I think I’ve taken more chances with this record than I have with some of the others. I think stylistically it sounds different. I was not as concerned with this record as to how it would be perceived. I was more concerned with the way it felt making it and how to geel that I was expressing parts of my personality that normally I would not ahve brought forth, or would’ve tried to polish up, or would have waited till it was more perfect. But this, I didn’t want to do that at this point in my life.

C: I’m surprised to hear you say that because it has an extremely polished feel, the record. It’ doesn’t sound like an improvisatio nat all.

S: No, no. It wasn’t even a fact of experimenting; it wasn’t as though I was trying to experiment with something wild. It was more a natural letting go of things that were already in there. And it was a question of doing what was right for each song. But, it also meant that the songs themselves had more extreme kinds of moods in them than they did before. I don’t know if a song like Blood Makes Noise ” – I dont know if five years ago I might have decided that song was too ugly to put on a record, cause there are other songs I have that I don’t put on records.

C: Oh, I see. Let’s look at Blood Makes Noise

S: I guess if I bring it up I should expect to have it discussed. This one says:
Blood makes noise
I’d like to help you doctor
Yes, I really, really would
But the din in my head it’s too much and it’s no good
I’m standing in a windy tunnel shouting through the roar
I’d like to give the information you’re asking for

C: But blood makes noise, it’s a ringing

S: in my ear
Blood makes noise
and I can’t really hear you in the thickening of fear
I think that you might want to know the details and the facts
but there’s something in my blood denies…

C: I would like to know the details and the facts, that’s what I’m trying to get to. Now you give me the answer, there’s something in your blood.

S: denies the memory of the act..

So just forget it, doc
I think it’s really cool that you’re concerned
but we’ll have to try again after the silence has returned

It’s kind of a stange way to address one’s doctor. It’s a little flip. It’s almost condescending.

C: It’s foolish if you’re sick.

S: It is foolish I suppose, if you’re sick.

C: Were you sick?

S: I have been sick in my life.

C: Yes? Some of the reviewers have observed that there’s a lot of medical inference and vocabulary.

S: Yeah. Well, some of that is my way of amusing myself and being what I call funny. It’s a very ovscure kind of humor. Some of it is because I think the language of medicine is fascinating and has its own poetry in it. And some of it I think is probably cause when I came off the road in 1987 I was, not seriously sick, I’m really healthy, but I was anemic and I had asthma and bronchitis and stuff you get from being run down. But I think the main reason I work with these terms is because I feel that language itself is beautiful, and especially medical language is a way of talking about the body in a way that’s intimate without being corny. Although I think I’ve probably taken it about as far as I’m going to take it. But, I do get letters from doctors.

C: So, what do they say?

S: Oh, they say that the information is very accurate and could they use the lyrics in their own texts, and…

C: ..could they meet you?

S: One or two want to know if they can meet me. Or they want to know how do I know so much about medicine.

C: Well, how do you know so much about medicine?

S: Cause I’m curious about the body, I’m curious about being healthy and I like the idea of ministering.

C: There’s a very beautiful line in one of your songs. I though it was really excellent, I underlined it, “I will pay for my life with my body.”

S: It’s a very sad line, in the context of it.

C: What does it mean?

S: Well the girl in the song, who is a girl with a secret, feels like the woman who walks in the street. And in that way she is in someway paying for her life with her body.

C: It’s a mysterious way to describe what we all do really.

S: I suppose everyone deos, ultimately.

C: We all pay for our lives with our body.

S: You mean in the final end of it, it is.

C: I mean that’s what we do, pay a little bit everyday.

S: Right

C: I thought it was a very beautiful line that is very much…

S: I mean some people are forced to pay more with their bodies than they might under other circumstances.

C: What is the “bad wisdom?”

S: The bad wisdom is exactly the things we were talking about before. The bad wisdom is knowing something before you’re ready for it. It’s knowing something before it’s time. Before. It could be sexual knowledge. Seom kids take LSD too early.

C: Did you?

S: No. Bad wisdom is when you have too much too soon. You go beyond what you’re prepared to handle.

C: To me it’s quite interesting how consistent the themes are in this record. Song after song we are really discussing the same song, and the same position in regards to the information in the song. Could you read this song, Bad Wisdom?

S: Okay.

C: You don’t have to if you don’t want to. I’m going to have another glass of wine. Would you like one?

S: I’ll have a little bit, yeah.

C: That’s the spirit.

S: I’ll have wine and I’ll have water.

C: the biblical beverages.

S: It sounds so much better than coke and orange juice, or one of those kinds of things.

C: It’s so very stylish of you to have just wine and water.

S: Well, thank you, Leonard. It also happens to be what is available.

C: Why won’t you tell me what you really know about what the bad wisdom is?

S: Because, when I write these songs I feel the important thing is that we know that they are truthful, and ti doesn’t matter. It shouldn’t matter to you, for example. If I’m putting these words out to be judged and I want the work to be judged, then I feel everything you need to know is in the work. There’s nothing you need to know about what I know. For someone to want to know, for example, how much of these songs do I… what are the things in my own life. To me that’s out of bounds then.

C: I completely agree with you.

S: Because then someone is open to judging my character and that’s not what I’m putting out, that’s not what I’m displaying. I’m putting the work out because the work is the work, and the work is what I hope is beautiful and good, and the work is what will be around after I’m not here anymore. Ant that to me seems like the important thing. The bad wisdom is what I said. It’s knowing about something too soon. In some ways, everybody has their own form of it.

C: Well, forgive me for asking you this question over and over again, but according to the instructions that this interview may be broadcast or a transcript prepared in segments of various lengths it is my intention to ask you the same question over and over again so no matter how the segments are divided the most important response to the album will be established.

S: So what question is it that you’re asking exactly?

C: I forget.

S: It all depends. The answer all depends on how it’s phrased and what exactly you want to know.

C: Now, obviously the primary theme of the interview is the new album. I think that we’ve treated that at some length.

S: I think we have too.

 

Part 3

C: The following list of topics is illustrative only. We would like you to touch on most of the issues so as to provide the content necessary to satisfy the promotional purpose of this piece. However, during the course of your conversation, please fell free to venture in any other areas that may come up.

S: Yes.

C: So, it was not entirely without permission that I was prodding in these areas, even though I understand that your aesthetic determines that, or is a kind of curtain, the kind of curtain you speak about, beyond which the viewer is not invited to look. There is this and this alone and this is the work and it should be judged as the work by itself without any reference to the hand that created it.

S: Yeah, I know that sounds cold.

C: That’s okay. I can take it.

S: It sounds cold, but ut’s the way I like it.

C: You like to write about anything you don’t really have to write about.

S: I like to write about things that are extreme in some form. I like to write about something I feel I have to write about.

C: Do you find it hard to write?

S: Sometimes.

C: What is the hardest song on this album?

S: The hardest song on this album was “99.9F”

C: That was hard to write? And yet, it comes off effortless.

S: That was the most difficult song. That was the song I was sitting there looking in the thesaurus and the rhyming dictionary with. Looking up synonyms and antonyms for hot, cold, fever, romance, anything I could get my hands on.

C: Is this a flirtatious song?

S: Yeah. Couldn’t you tell? You couldn’t tell.

C: Well, I’m immune to these kinds of approaches.

S: Oh, I see.

C: I thought it was very lovely, and to repeat that phrase “ninety-nine point nine” was very fresh. Let’s look at this song.

S: Okay.

C: Why did you call this the title of your album?

S: Because I felt that it described the stance of the album, which is not normal, off the norm, not wildly feverish but off the norm enough to create tension, enough to give you a straight dizzy hallucinatory feeling but not so much that you feel that you’re out of your mind in listening to it. It seemed slightly hotter than maybe some of my other albums. the other albums have a much cooler tone to the whole sound of them.

C: A cautious intoxication.

S: Yeah, I guess so.

C: Do you drink?

S: Yeah.

C: What do you usually like to drink?

S: Well, let’s see, these days I drink gin and tonic. I drink wine or I drink cognac or I drink brandy or I drink sake. I have a bunch of things that I like to drink.

C: Do you find that a lot of people are drinking now, these days?

S: I find that most of the people I hang out with tend to drink but I think that’s also because that’s the kind of crowd I hang out with. I drink Jack Daniels.

C: What are the people like in your crowd?

S: Oh god. It’s a very diverse crowd, I suppose. It’s not even really like a crowd, its more like a thinly, sparsely populated little gathering of forlorn and homeless people.

C: Where do they live? Is it nationwide or is your crowd more or less in one town?

S: My crowd. Some live in New York and some live in California, and some are people I used to know from the folk scene and I’m still friends with them. In the village. And some are new friends I made last year, and there’s been some pretty wild drinking going on there. Drink to six, seven, eight the next morning.

C: Among your new friends?

S: Yeah, among my new friends.

C: Your new friends drink a lot.

S: Yes, yes we do. And I drink with them.

C: Could you let us in on one of these evenings. these drinking evenings. How do they begin? What is the middle like? And what is the ending like?

S: The beginning usually means me going to pick my sister and she comes with me, or my brother, because they all like to hang out. We’re talking about a party now, not talking about an intimate social gathering, this is a party.

C: I’d really like to know what an evening where a lot of liquor is consumed…

S: With me it usually ends up in wild dancing.

C: Yeah? Begins early and ends late?

S: Yes. I really love to dance.

C: What music do you dance to?

S: I used to dance to your music actually, when I was younger, seventeen or so. You’ll laugh at the songs I chose to dance to, they’re not what you’d think of as dancing songs.

C: On the contrary, others may not think so, but you and I know what a dancing song is.

S: So Long Maryann, or The Avalanche Song, or The Master Song.

C: What are you dancing to these days?

S: There’s a band called Les Negresses Vertes, which is a terrible French pronunciation on my part of their title. It’s almost like gypsy music. I’ll dance to that. What else will I dance to? I dance to some of the new U2 albums. Sometimes I’ll dance to … PM Dawn has a song called Paper Doll which I like. Or different things that come up or catch my imagination in some way.

C: And when you go out with friends, when you’re with your crowd, how are people dressing in your crowd?

S: Well, I have a friend of mine who makes dresses, she tends to make these big linen dresses and pants, they’re loose and baggy and usually made out of cotton or linen or something like that… they’re almost peasant-like.

C: Do you wear them too?

S: Yeah, I wear them often.

C: They have pants underneath the skirt?

S: Either pants, which are baggy, they’re like farmers pants. See, I’ll show you the art work of the album cover and I’m wearing some of her clothes.

C: What’s that?

S: That’s the album cover.

C: That’s the vinyl?

S: Yeah, yeah, that is the vinyl. Here, I’ll show you, hold on a second.

C: Okay. Hold on everybody, Suzanne is dancing across the room.

S: These are some of her pants.

C: Oh, that’s a very nice picture.

S: They’re baggy and they’re really cool.

C: Those are your arms.

S: Those are my arms.

C: And what is your expression?

S: This is the expression… I don’t know. What would you describe it as? “What the hell are you looking at?” kind of face?

C: That is the extremely seductive… this combination of austerity and voluptuousness that your songs manage to convey. that is a refined invitation to a cautious intoxication.

S: Well, thank you, Leonard. As opposed to the other. Here, let me show you the other poster that I see lying on the floor.

C: Okay. Suzanne is now going over to get the other poster that is lying on the floor.

S: These are the fishnet stockings from the dancing girl. The dancing girl on the album is wearing fishnet stockings and these are them blown up.

C: I know. This has many resonances of self-abuse.

S: Self-abuse?

C: Yes.

S: I don’t think so.

C: I think you might be making a pass at yourself in this.

S: No, Leonard, you’ve got it all wrong.

C: Oh, I’m sorry.

S: You’ve let your imagination go too far. This is my shoe. This is my shoe. This is my knee.

C: I’m sorry. I’m sorry. I’m terribly sorry. But, I think that anybody’s imagination is being invited to really careen around the place.

S: Well, we’ll put this one away then.

C: Yeah, please do. A man of my age should not be compelled to look at those kind of photos.

S: So anyway, those are Morgan’s pants. What I started to show you were Morgan’s pants, what they look like.

C: Are you choosing your intimate male partners from among the members of this crowd? Or, do they come drifting in, they belong to other crowds?

S: No.

C: … sometimes no crowd at all.

S: I would say no crowd at all, really.

C: Guys stumble into your life from…

S: No, I wouldn’t say they stumble in. You know, you asked me in the beginning if I was a guarded person, and I guess I’m sort of a guarded person.

C: I thought so until I saw those fishnet stockings. That’s changed things a lot. I wish I saw that at the beginning of this interview.

S: But that’s a character.

C: No, I really don’t think you can use that as an alibi.

S: Oh.

C: This is you in fishnet stockings, you cannot sanitize this image.

S: Oh, I didn’t say I was going to sanitize it, I just said I was in character.

C: No, I’m sorry Suzanne.

S: It’s not a very clean character, but…

C: You’re not in character at all. Forst of all, there’s no enough showing to indicate a costume that could even indicate a character.

S: That’s because you saw an isolated detail there. You haven’t seen the whole context of it.

C: You mean that’s just part of the poster?

S: No the actual picture the costume is from is… there’s a real picture.

C: Yes, but when you select your hand and a fishnet stocking and nothing else, people cannot be faulted if they don’t assume you’re in costume.

S: Well, they would if they… Okay, whatever. I could fault them if I want to.

C: You can do anything you want. Would you like to talk about Mitchell Froom?

S: Would I like to talk about Mitchell Froom?

C: Yes, because the production is really extremely competent and beautiful. What was his contribution?

S: Well the reason I wanted to work with him was because I could tell from his other records that he didn’t approach anything in a formulaic way, and that seemed like a good thing to me.

C: Would you like to talk about the other people that worked with you?

S: Yeah, we could do that. I think that the musicians that we used on this album, besides using… on one track we used Mike Visceglia and Marc Shulman who are my long term musicians that I’ve used for a long time. But the newer musicians are Bruce Thomas, who played with Elvis Costello for ten years, he was in The Attractions, the band. Do you like Elvis Costello or do you listen to him?

C: I’ve listened to him a lot. He’s a great singer.

S: So, I’ve always liked the way his band sounded. To me it’s very witty and it’s got a lot of interesting things about it. And Jerry Marotta played percussion and drums.

C: Do you get along well with your musicians?

S: Yeah, I do.

C: And when you tour do you feel part of a family?

S: It has felt that way sometimes, not always, but most of the time, yeah. I do, I like it, I like the atmosphere that develops.

C: You like touring?

S: I like a lot of it. The last one was a little long.

C: How many concerts did you do?

S: I did ten months.

C: Ten months on the road?

S: Ten months on the road, sometimes five shows a week.

C: How many concerts altogether did you do?

S: I don’t remember.

C: Hundreds.

S: Hundreds, yeah. Well, there’s fifty-two weeks in a year, tem months… forty weeks, but it wasn’t really forty weeks, it was more like thirty.

C: Let’s say thirty weeks, let’s say an average of three concerts a week…

S: Ninety?

C: Nine hundred. It’s nine thousand concerts, I think.

S: We can study my itinerary if we want to count them. A lot, a lot, but there’s a lot about it that I love. It is like a family and I get to know…. I’m on the road with seventeen guys that I have to know about in some way or another, and know about their lives, and what’s happening with their lives. Who’s having problems and who’s doing well, and who’s just ahd a baby and who’s mother is sick. I enjoy that kind of feeling, of getting to know people and getting to know their character.

C: And do you feel that you occupy some maternal function on the road, that you kind of hold it together with these concerns that you just mentioned? That you are the center of the family?

S: I’m definitely the center of the family. I suppose that makes it maternal. Sometimes I feel more like the figurehead of the ship, and the engine. Maternal’s not quite the word cause that implies a certain coziness which is not really always there. There’s still always a bit of distance and formality, but I like the atmosphere. I like staying up and drinking and playing poker and tlking and that kind of thing.

C: You like that?

S: I like the feeling of being on adventure, of being on the bus overnight on a ferry and we’re going somewhere, we’re going to Greece or we’re going to Italy and this feeling of a shared adventure.

C: You’re lucky.

S: Why is that?

C: You’re lucky to have this experience.

S: Do you like touring?

C: Yeah, I like it. I kind of feel like part of a motorcycle gang.

S: Yeah, I could see that.

C: What plans do you have to tour?

S: Probably early next year.

C: Do you have your band put together yet?

S: No.

C: Can I play in it?

S: What would you like to play?

C: I don’t know.

S: You could sing, you could be a backup singer.

C: Congas

S: It’s like I always go see you perform, you always have two very beautiful women standing by you.

C: I could be one of the beautiful women standing beside you.

S: I could have you standing behind me singing.

C: Oh, that would be a great honor. What kind of live show can be expected?

S: Well, you’ll come with me and we can sing duets, we can dance.

C: Oh, that would be really nice.

C: Now your expectations and feelings about the album, we’ve looked into that, but I think if you would speak about your expectations… but really honestly about your expectations.

S: What do you mean, “but really honestly” as though I’ve…

C: It’s not that I geel you’ve been dishonest in any sense.

S: Well that’s good because I haven’t.

C: No, I don’t feel you’ve been dishonest.

S: No.

C: But there is this tantalizing and …

S: …irritating?

C: …irritating is not quite the word, let’s say intriguing sense of secrecy that you insist on preserving.

S: I bet.

C: I’d really like to know what you expect from this album, but really deeply. Do you think that this album will bring you the lover?

S: It’s possible.

C: Do you think of it as a mating call? Do you see this album as a mating call?

S: Why? Do you see it that way?

C: Yes, yes I do.

S: Do I see it as a mating call? As a mating call?

C: Yes, I see this album as an exquisite, refined mating call of one of the most delicate and refined and concealed creatures on the scene. This is the mating call of concealment. This is how secrecy woos her lover. So, do you think that this album will bring you the lover which the album calls out to?

S: Yes.

C: I do too. I really do. I think we are at last approaching the truth of the enterprise. This no doubt will not find it’s way into…

S: I think you’re wrong. I think they’ll make a headline out of it in fact. But without saying anymore than that, I would say, “yes”.

C: I think so. I find it irresistable myself.

S: On that cheery note, that’s a cheery note to end on.

C: I think so

 

Reference

Suzanne’s interview with Leonard Cohen from 1992 (part 3 of 3)

 

同时考虑阳性对照和安慰剂对照的设计

 

一个试验同时涉及阳性对照和安慰剂对照的设计,目的主要是证明:试验药非劣于阳性对照药,并且试验药优于安慰剂。看起来很奇怪,当试验药非劣于阳性对照药时,不就间接反映试验药优于安慰剂了吗?为什么不直接设计个非劣效试验,而选择同时证明试验药优于安慰剂呢?我也没搞清楚为什么,在网上也没查询到相关文献,基于个人理解推断可能有以下两点原因:1. 非劣试验如果最后证明了试验药不仅非劣,甚至达到了优效,那这结果会很有说服力,说明试验药效果比较好,比阳性对照药好,那肯定比安慰剂要好。但是如果只达到非劣并未达到优于阳性对照药,这时可能会有人问试验药是否比安慰剂好呢?虽然可以用非劣于阳性对照药作为间接证据证明试验药比安慰剂要好,但是证据强度比较低,直接证据还是挺重要的;2. 如果试验药做出来的结果是劣于阳性对照药,那肯定是不可能被批准了;此时,如果在设计之初同时考虑了对比安慰剂,假如最后做出的结果非劣于阳性对照药但优于安慰剂。虽然这结果也不太好,但如果该药有其他优势,比如服用起来比较容易,还是有可能被批准的。

 

那一个试验同时设计阳性对照和安慰剂对照时,如何布局这两个指标呢?两个指标同时作为主要指标,还是一个作为主要指标另一个作为次要指标呢,谁先谁后呢?选择什么样的设计策略,要根据指标的重要程度以及对药物效果的了解程度来做决策。下面三种不同策略以及优缺点。

 

1.Co-primary endpoint 两个指标同时作为主要指标

https://clinicaltrials.gov/ct2/show/NCT01464905?term=non-inferiority%2C+superiority&draw=3&rank=19

1.PNG

该方法把两个指标视为同等重要,没有先后之分。采用all or nothing策略,不需要调整一类错误。此时,两个指标需要同时达到p值小于0.05才算有统计学意义。

 

2.顺序法1:非劣于阳性对照药作为主要指标,优于安慰剂作为次要指标

https://clinicaltrials.gov/ct2/show/NCT03332771?term=non-inferiority%2C+superiority&draw=6&rank=47

1.PNG

此时,按照顺序先检验主要指标,主要指标达到P值小于0.05后,再检验次要指标。如果主要指标未达到统计学意义,次要指标不再进行检验。所以,无需调整一类错误。采用此种方法,会出现如下几种情况:

 

  • 主要和次要指标都达到统计显著:试验药非劣于阳性对照药,并且优于安慰剂。
  • 主要指标达到统计显著,次要指标未达到:试验药非劣于阳性对照药,但不优于安慰剂
  • 主要指标未达到统计显著:试验药物劣于阳性对照药,次要指标不再需要检验

 

出现2.1情况,说明试验药可以被批准,试验成功;出现2.3情况,说明试验药不能被批准,试验失败,说明试验药劣于阳性对照药,至于是否优于安慰剂已没有机会检验;出现2.2情况,是比较纠结的情况:此时的情况非常奇特,按道理说,试验药非劣于阳性对照药,可以间接证明试验药优于安慰剂;当直接证据未能证明试验药优于安慰剂时,需要从以下几个方面寻找原因:1. 试验实施的质量是否存在问题;2. 非劣效界值是否偏大。

 

3. 顺序法2:优于安慰剂作为主要指标,非劣于阳性对照药作为次要指标

https://clinicaltrials.gov/ct2/show/NCT03351478?term=non-inferiority%2C+superiority&rank=73

1.PNG

https://clinicaltrials.gov/ct2/show/NCT00408187?term=non-inferiority%2C+superiority&draw=8&rank=149

1.PNG

此时,按照顺序先检验主要指标,主要指标达到P值小于0.05后,再检验次要指标。如果主要指标未达到统计学意义,次要指标不再进行检验。所以,无需调整一类错误。采用此种方法,会出现如下几种情况:

 

  • 主要和次要指标都达到统计显著:试验药优于安慰剂,并且非劣于阳性对照药
  • 主要指标达到统计显著,次要指标未达到:试验药优于安慰剂,但劣于阳性对照药
  • 主要未达到统计显著:试验药物不优于安慰剂,次要指标不再需要检验

 

出现情况3.1说明试验成功;出现情况3.3说明试验失败,试验药不优于安慰剂,此时次要指标不再需要检验,也没有必要去检验是否非劣于阳性对照药,对于一个不优于安慰剂的药而言估计大概率是劣于阳性对照药的。 出现情况3.2是值得讨论的:此时试验药优于安慰剂,但劣于阳性对照药,至于能否被批准,得看该药物的其他优势。该策略把优于安慰剂放在,比非劣于阳性对照药更重要的位置,是否能被药监机构认可,有待商榷。

 

我们到底应该选择哪种策略:

 

a. 如果两个指标同等重要,都需要达到统计显著性才能被批准:选择把两个指标都作为主要指标,并且选用All or nothing方法

b. 如果非劣于阳性对照药更重要:选择非劣于阳性对照药作为主要指标,优于对照药作为次要指标,按照顺序进行检验

c. 如果优于安慰剂更重要:选择优于安慰剂作为主要指标,非劣于阳性对照药作为次要指标,按照顺序进行检验。

d. 如果两个指标的优先级可以任意分配,按照检验效能排序:策略4最好,其次是策略3,最后是策略1;假如试验药优于安慰剂,但是劣于阳性对照药。采用策略4,试验药有机会被批准上市;选择策略3或者策略1,都没有机会被批准。

 

看一个更新颖的设计:对比阳性药优效未达到时,若试验药优于安慰剂,评估是否非劣于阳性对照药。

https://clinicaltrials.gov/ct2/show/NCT00094549?term=non-inferiority%2C+superiority&draw=4&rank=190

2.png

 

Machine Learning Competion

 

1.Heart prediction


“`{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
“`

# 1. Dataset Viewing

This part aims to view the dataset so that we can have a general knowledge of training datasets.

“`{r heart}

library(dplyr)

heart<-read.csv(file=”C:/Users/212697818/Desktop/Datadriven Project/ML Heart/train_values.csv”,header=T)

heart_labels<-read.csv(file=”C:/Users/212697818/Desktop/Datadriven Project/ML Heart/train_labels.csv”,header=T)

total<-merge(heart, heart_labels, by=”patient_id”)
total$thal= case_when(total$thal==”normal” ~ 1,
total$thal==”reversible_defect” ~ 2,
total$thal==”fixed_defect” ~ 3)

attach(total)

# list the variables in heartdata
names(total)

# list the structure of heartdata
# str(total)

#dimensions of heartdata
dim(total)

# obs number of patients with heart disease
dim(total[total$heart_disease_present==1, ])

# obs number of patients without heart disease
dim(total[total$heart_disease_present==0, ])

# print first 5 rows of heartdata
# head(heart, n=5)

# print last 5 rows of heartdata
# tail(heart, n=5)

“`

# 2. Statistical test

In this part, we test the differences of 13 variables between the heart disease group (heart_disease_present=1) and non-disease group (heart_disease_present=0) seperately, and then test the correlation between covariate and outcome variable. The box plot is also demonstrated in this part.

## 2.1 Test differences of resting blood pressure between group heart disease and group with no heart disease

“`{r blood}

t.test(resting_blood_pressure~heart_disease_present)$p.value
model <- glm(heart_disease_present ~resting_blood_pressure,family=binomial(link=’logit’), data=total)
summary(model)$coefficients

#plot
boxplot(resting_blood_pressure~heart_disease_present,data=total,
xlab=”heart disease present”, ylab=”resting blood pressure”)
“`
## 2.2 Test differences of serum cholestoral between group heart disease and group with no heart disease

“`{r cholestoral}

t.test(serum_cholesterol_mg_per_dl~heart_disease_present)$p.value
model <- glm(heart_disease_present ~serum_cholesterol_mg_per_dl,family=binomial(link=’logit’), data=total)

summary(model)$coefficients

#plot
boxplot(serum_cholesterol_mg_per_dl~heart_disease_present,data=total,
xlab=”heart disease present”, ylab=”serum cholestoral”)

“`

## 2.3 Test differences of age between group heart disease and group with no heart disease

“`{r age}

t.test(age~heart_disease_present)$p.value
model <- glm(heart_disease_present ~age,family=binomial(link=’logit’), data=total)

summary(model)$coefficients

#plot
boxplot(age~heart_disease_present,data=total,
xlab=”heart disease present”, ylab=”age”)

“`
## 2.4 Test differences of maximum heart rate between group heart disease and group with no heart disease

“`{r heartrate}

t.test(max_heart_rate_achieved~heart_disease_present)$p.value
model <- glm(heart_disease_present ~max_heart_rate_achieved,family=binomial(link=’logit’), data=total)

summary(model)$coefficients

#plot
boxplot(max_heart_rate_achieved~heart_disease_present,data=total,
xlab=”heart disease present”, ylab=”maximum heart rate”)

“`

## 2.5 Test differences of ST depression between group heart disease and group with no heart disease

“`{r depression}

t.test(oldpeak_eq_st_depression~heart_disease_present)$p.value
model <- glm(heart_disease_present ~oldpeak_eq_st_depression,family=binomial(link=’logit’), data=total)

summary(model)$coefficients

#plot
boxplot(oldpeak_eq_st_depression~heart_disease_present,data=total,
xlab=”heart disease present”, ylab=” ST depression induced by exercise relative to rest”)

“`

## 2.6 Test number major vessels between two groups

“`{r vessels}

table(total$heart_disease_present, total$num_major_vessels)
chisq.test(total$heart_disease_present, total$num_major_vessels)
model <- glm(heart_disease_present ~num_major_vessels,family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`

## 2.7 Test thal between two groups

“`{r thal}

table(total$heart_disease_present, total$thal)
chisq.test(total$heart_disease_present, total$thal)

model <- glm(heart_disease_present ~thal,family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`
## 2.8 Test slope of peak exercise between two groups

“`{r slope}

table(total$heart_disease_present, total$slope_of_peak_exercise_st_segment)
chisq.test(total$heart_disease_present, total$slope_of_peak_exercise_st_segment)

model <- glm(heart_disease_present ~ slope_of_peak_exercise_st_segment,family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`

## 2.9 Test chest pain between two groups

“`{r pain}

table(total$heart_disease_present, total$chest_pain_type)
chisq.test(total$heart_disease_present, total$chest_pain_type)
model <- glm(heart_disease_present ~ chest_pain_type, family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`

## 2.10 Test blood sugar between two groups

“`{r sugar}

table(total$heart_disease_present, total$fasting_blood_sugar_gt_120_mg_per_dl)
chisq.test(total$heart_disease_present, total$fasting_blood_sugar_gt_120_mg_per_dl)

model <- glm(heart_disease_present ~ fasting_blood_sugar_gt_120_mg_per_dl, family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`
## 2.11 Test ECG between two groups

“`{r ecg}

table(total$heart_disease_present, total$resting_ekg_results)
chisq.test(total$heart_disease_present, total$resting_ekg_results)

################################################
# logistic model
################################################

model <- glm(heart_disease_present ~ resting_ekg_results, family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`
## 2.12 Test Sex between two groups

“`{r sex}

table(total$heart_disease_present, total$sex)
chisq.test(total$heart_disease_present, total$sex)

model <- glm(heart_disease_present ~ sex, family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`

## 2.13 Test angima between two groups

“`{r angima}

table(total$heart_disease_present, total$exercise_induced_angina)
chisq.test(total$heart_disease_present, total$exercise_induced_angina)
model <- glm(heart_disease_present ~ exercise_induced_angina, family=binomial(link=’logit’), data=total)

summary(model)$coefficients

“`

#3. Model building and evaluating

Training dataset is split into two datasets: training and testing (validation) datasets. The model is trained on the training dataset and then evaluated on the testing (validation) dataset using accuracy/sensitivity/specificity rates. Six methods – gradient boosting, decision tree, random forest, SVM, logistic model, naive bayes are used to build model for training dataset and then evaluated on the testing dataset. The accuracy rates from six models are compared.

## 3.1 Gradient boosting model
“`{r gbm}
# Load packages
library(caret)
library(gbm)
library(e1071)
library(randomForest)
library(adabag)
library(rpart)

#################################################
# data prep
#################################################

# convert outcome binary variable as factor variable
total$heart_disease_present<-as.factor(total$heart_disease_present)
#################################################
# model it
#################################################
# split data into training and testing chunks
set.seed(825)
inTrain <- createDataPartition(
y = total$heart_disease_present,
## the outcome data are needed
p = .75,
## The percentage of data in the
## training set
list = FALSE
)

training <- total[ inTrain,]
testing <- total[-inTrain,]

# Drop variable patient_id
training<-select(training, -c(patient_id))

# create caret trainControl object to control the number of cross-validations performed
gbmControl <- trainControl(method =”repeatedcv”,
number = 20, repeats=20)

gbmGrid <- expand.grid(interaction.depth = c(1, 2, 3),
n.trees = (1:5)*50,
shrinkage = 0.05,
n.minobsinnode = 10)

# Build model
gbmFit1 <- train(heart_disease_present ~ ., data = training,
method = “gbm”, trControl = gbmControl,
verbose=FALSE, tuneGrid = gbmGrid)

trellis.par.set(caretTheme())
plot(gbmFit1)
# plot(gbmFit1, metric = “Kappa”)
# plot(gbmFit1, metric = “Kappa”, plotType = “level”, scales = list(x = list(rot = 90)))
#################################################
# evalutate model
#################################################

# class prediction
gbm_predict<-predict(gbmFit1, newdata = testing)

# Variable importance
gbmImp <- varImp(gbmFit1, scale = FALSE)
gbmImp

plot(gbmImp, top = 13)
# Measures for predicted classes
gbm_predict <- sort(gbm_predict)
confusionMatrix(data = gbm_predict, reference = testing$heart_disease_present )
“`

## 3.2 Decision Tree

“`{r dt}

set.seed(1234)

dt1<-rpart(heart_disease_present~.,data=training,method=”class”)

dt_predict<-predict(dt1,newdata=testing,type=”class”)

confusionMatrix(data=dt_predict, reference=testing$heart_disease_present)

“`

# 3.3 Random Forest

“`{r rf}

cv<- createMultiFolds(training, k = 10, times = 10)
rfControl <- trainControl(method = “repeatedcv”, number = 10, repeats = 10,index = cv)

rf1 <- train(heart_disease_present ~ ., data = training, method = “rf”, trControl = rfControl,
verbose=FALSE,
tuneLength=5, # 5 can get the maximum accurary rate
ntree=300) # 300 can get the maximum accurary rate

rf_predict<-predict(rf1,newdata=testing)
confusionMatrix(data=rf_predict, reference=testing$heart_disease_present)
“`
## 3.4 SVM

“`{r SVM}

set.seed(1274)
svm<-tune.svm(heart_disease_present~.,data=training,kernel=”linear”,cost=c(0.01,0.1,0.2,0.5,0.7,1,2,3,5,10,15,20,50,100))
svm

###Lets get a best.liner model
best_svm<-svm$best.model

##Predict using test data
svm_predict<-predict(best_svm,newdata=testing)
confusionMatrix(data=svm_predict, reference=testing$heart_disease_present)
“`
## 3.5 Logistic model

“`{r logic}
# Model Building
logic<-glm(heart_disease_present~.,data=training, family=binomial(link=logit))

confint(logic)
# Predict testing data

logic_predict <- predict(logic, newdata=testing, type =”response”)

p0.5<-logic_predict>0.5
p0.5 = case_when(p0.5==”TRUE” ~ 1,
p0.5==”FALSE” ~ 0)

logicresult<-table(data=p0.5, reference=testing$heart_disease_present)

rate<-function(x){

accuracy<-(x[1, 1]+x[2, 2])/(x[1, 1]+x[2, 2]+x[1, 2]+x[2, 1])
Sensitivity=x[2, 2]/(x[2, 2]+x[1, 2])
Specificity=x[1, 1]/(x[1, 1]+x[2, 1])
Pos.Pred.Value=x[2, 2]/(x[2, 2]+x[2, 1])
Neg.Pred.Value=x[1, 1]/(x[1, 1]+x[1, 2])
result<-list(accuracy=accuracy,
Sensitivity=Sensitivity, Specificity=Specificity,
Pos.Pred.Value=Pos.Pred.Value, Neg.Pred.Value=Neg.Pred.Value)
return(result)
}

logicresult

rate(logicresult)
“`
## 3.6 Naive Bayes

“`{r nb}
nb<-naiveBayes(heart_disease_present~., data=testing)

nb_predict <- predict(nb, newdata=testing)

nbresult<-table(data=nb_predict, reference=testing$heart_disease_present)

nbresult

rate(nbresult)
“`
## 3.7 Summary

The accuracy rates from the six models are compared and ranked from high to low as below:

* Ranking 1: Naive Bayes 0.978

* Ranking 2: Logistic Model 0.911

* Ranking 2: SVM 0.911

* Ranking 4: Random forest 0.889

* Ranking 5: Decision Tree 0.733

* Ranking 6: Gradient Boosting 0.511

In order to test the stablity of above rankings, other percentages are chosen to split the training dataset. Changing the spliting percentage from 0.75 to 0.5 or 0.8 or 0.9 using function createDataPartition(), the ranking orders keep unchanged.

# 4. Next step …

Which model is best for predicting the heart disease? It seems that naive bayes model is best predicting model when spliting percentage is 0.75; logistic model and SVM rannked 2. The reason why simple models, like naive bayes or logistic model, have good performance may be due to that the data dimensionality is small. But can we predict heart disease using naive bayes model directly without further mining? We have no high confidence currently. What do we need to do in next step? Parameter-tuning for other models (Random forest, gradient boosting and etc), adoption of deep-learning method, re-evaluation and validation may be the feasible way to advance the predicting model in next step.
# 5. Reference
https://www.kaggle.com/hiteshp/head-start-for-data-scientist/code

 

2. House Price



title: “HousePrice”
author: “Ning Li”
date: “2019/8/19”
output: word_document

“`{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
“`

## Introduction
This document aims to demonstrate how to do data analysis with R using an example on house price prediction from kaggle.
## Step 1: install and upload R packages
Before starting to do data analysis, we firstly install and upload all the important R packages into the R platform. Then, we import the train dataset into R.

“`{r start}

# Upload R packages

library(randomForest)
library(rpart)
library(caret)
library(anchors)
library(readr)
library(ggplot2)
library(ggthemes)
library(scales)
library(gridExtra)
library(e1071)
library(corrplot)
library(GGally)
library(gbm)
# Import datasets

train <- read.csv(“C:/Users/212697818/Desktop/HousePredict/train.csv”)
test <- read.csv(“C:/Users/212697818/Desktop/HousePredict/test.csv”)
test$SalePrice<-rep(NA,1459)
# Cobine train and test datasets
train$isTrain <- 1
test$isTrain <- 0

house <- rbind(train,test)

“`
## Step 2: Dataset Viewing

This part aims to view the dataset so that we can have a general knowledge of training datasets.

“`{r dataset}
# list the variables in dataset
names(train)

# list the structure of dataset
str(train)

#dimensions of dataset
dim(train)

# obs number of house build in 1970
dim(train[train$YearBuilt==1970, ])

# print first 5 rows of heartdata
head(train, n=5)

# print last 5 rows of heartdata
tail(train, n=5)

# The percentage of data missing in train
sum(is.na(train)) / (nrow(train) *ncol(train))

# Check for duplicated rows
cat(“The number of duplicated rows are”, nrow(train) – nrow(unique(train)))
“`

## Step 3: Data visulization
We create one train dataset with categorical variables and one with numeric variables and use these two datasets for data visualization.

“`{r visualization}
# Numeric variables
numeric_var <- names(train)[which(sapply(train, is.numeric))]
train_num<-train[numeric_var]

# Character variables
cat_var <- names(train)[-which(sapply(train, is.numeric))]
train_cat<-train[cat_var]
# Box Plot between the neighboorhodds and sale price
ggplot(train, aes(x = Neighborhood, y = SalePrice)) +
geom_boxplot() +
geom_hline(aes(yintercept=80),
colour=’red’, linetype=’dashed’, lwd=2) +
scale_y_continuous(labels=dollar_format()) +
theme_few()
## Do loop including Bar plot/Density plot function

## Bar plot function

plotHist <- function(data_in, i)
{
data <- data.frame(x=data_in[[i]])
p <- ggplot(data=data, aes(x=factor(x))) + stat_count() + xlab(colnames(data_in)[i]) + theme_light() +
theme(axis.text.x = element_text(angle = 90, hjust =1))
return (p)
}
## Density plot function

plotDen <- function(data_in, i){
data <- data.frame(x=data_in[[i]], SalePrice = data_in$SalePrice)
p <- ggplot(data= data) + geom_line(aes(x = x), stat = ‘density’, size = 1,alpha = 1.0) +
xlab(paste0((colnames(data_in)[i]), ‘\n’, ‘Skewness: ‘,round(skewness(data_in[[i]], na.rm = TRUE), 2))) + theme_light()
return(p)

}

## Function to call both Bar plot and Density plot function

doPlots <- function(data_in, fun, ii, ncol=3)
{
pp <- list()
for (i in ii) {
p <- fun(data_in=data_in, i=i)
pp <- c(pp, list(p))
}
do.call(“grid.arrange”, c(pp, ncol=ncol))
}

# Bar plot for category variables
doPlots(train_cat, fun = plotHist, ii = 1:6, ncol = 3)
doPlots(train_cat, fun = plotHist, ii = 7:12, ncol = 3)
doPlots(train_cat, fun = plotHist, ii = 13:18, ncol = 3)

#Density plot for numeric variables
doPlots(train_num, fun = plotDen, ii = 2:6, ncol = 2)
doPlots(train_num, fun = plotDen, ii = 7:12, ncol = 2)
doPlots(train_num, fun = plotDen, ii = 13:17, ncol = 2)
doPlots(train_num, fun = plotHist, ii = 18:23, ncol = 2)
# Correlation plot
correlations <- cor(na.omit(train_num[,-1]))
corrplot(correlations, method=”square”)
“`

The boxplot between the neighboorhoods and sale price shows that BrookSide and South & West of Iowa State University have cheap houses. While Northridge and Northridge Heights are rich neighborhoods with several outliers in terms of price.
## Step 4: Outcome data viewing

Let us lookd at the distribution and summary of the target outcome variables and detect if there are any outliers.

“`{r outcome}

summary(train$SalePrice)

hist(train$SalePrice)

ggplot(train,aes(y=SalePrice,x=GrLivArea))+geom_point()

summary(train$GrLivArea)

train <- train[train$GrLivArea<=4000,]

“`

## Step 5: Identify variables with missing values and impute missing values

This part aims to indentify the variables with missing values, and then impute missing values with “None” for category variables and impute missing values using mean value or median value for numeric variables.

“`{r impute}

Missing_indices <- sapply(house,function(x) sum(is.na(x)))
Missing_Summary <- data.frame(index=names(house),Missing_Values=Missing_indices)
Missing_Summary[Missing_Summary$Missing_Values > 0,]

house$MasVnrArea[which(is.na(house$MasVnrArea))] <- mean(house$MasVnrArea,na.rm=T)

house$Alley<-as.character(house$Alley)
house$Alley[which(is.na(house$Alley))] <- “None”

house$MasVnrType[which(is.na(house$MasVnrType))] <- “None”

house$LotFrontage[which(is.na(house$LotFrontage))] <- median(house$LotFrontage,na.rm = T)

house$FireplaceQu<-as.character(house$FireplaceQu)
house$FireplaceQu[which(is.na(house$FireplaceQu))] <- “None”

house$PoolQC<-as.character(house$PoolQC)
house$PoolQC[which(is.na(house$PoolQC))] <- “None”

house$Fence<-as.character(house$Fence)
house$Fence[which(is.na(house$Fence))] <- “None”

house$MiscFeature<-as.character(house$MiscFeature)
house$MiscFeature[which(is.na(house$MiscFeature))] <- “None”

house$GarageType<-as.character(house$GarageType)
house$GarageType[which(is.na(house$GarageType))] <- “None”

house$GarageYrBlt[which(is.na(house$GarageYrBlt))] <- 0

house$GarageFinish<-as.character(house$GarageFinish)
house$GarageFinish[which(is.na(house$GarageFinish))] <- “None”

house$GarageQual<-as.character(house$GarageQual)
house$GarageQual[which(is.na(house$GarageQual))] <- “None”

house$GarageCond<-as.character(house$GarageCond)
house$GarageCond[which(is.na(house$GarageCond))] <- “None”

house$BsmtQual<-as.character(house$BsmtQual)
house$BsmtQual[which(is.na(house$BsmtQual))] <- “None”

house$BsmtCond<-as.character(house$BsmtCond)
house$BsmtCond[which(is.na(house$BsmtCond))] <- “None”

house$BsmtExposure<-as.character(house$BsmtExposure)
house$BsmtExposure[which(is.na(house$BsmtExposure))] <- “None”

house$BsmtFinType1<-as.character(house$BsmtFinType1)
house$BsmtFinType1[which(is.na(house$BsmtFinType1))] <- “None”

house$BsmtFinType2<-as.character(house$BsmtFinType2)
house$BsmtFinType2[which(is.na(house$BsmtFinType2))] <- “None”

house$Electrical<-as.character(house$Electrical)
house$Electrical[which(is.na(house$Electrical))] <- “None”
# Factorizing
house$MSZoning<- factor(house$MSZoning)
house$Street <- factor(house$Street)
house$LotShape <-factor(house$LotShape )
house$LandContour<-factor(house$LandContour)
house$Utilities<-factor(house$Utilities)
house$LotConfig<-factor(house$LotConfig)
house$LandSlope<-factor(house$LandSlope)
house$Neighborhood<-factor(house$Neighborhood)
house$Condition1<-factor(house$Condition1)
house$Condition2<-factor(house$Condition2)
house$BldgType<-factor(house$BldgType)
house$HouseStyle<-factor(house$HouseStyle)
house$RoofStyle<-factor(house$RoofStyle)
house$RoofMatl<-factor(house$RoofMatl)
house$Exterior1st<-factor(house$Exterior1st)
house$Exterior2nd<-factor(house$Exterior2nd)
house$ExterQual<-factor(house$ExterQual)
house$ExterCond<-factor(house$ExterCond)
house$Foundation<-factor(house$Foundation)
house$Heating<-factor(house$Heating)
house$HeatingQC<-factor(house$HeatingQC)
house$CentralAir<-factor(house$CentralAir)
house$KitchenQual<-factor(house$KitchenQual)
house$Functional<-factor(house$Functional)
house$PavedDrive<-factor(house$PavedDrive)
house$SaleType<-factor(house$SaleType)
house$SaleCondition<-factor(house$SaleCondition)
house$Alley<-factor(house$Alley)
house$BsmtQual <-factor(house$BsmtQual)

house$BsmtCond<-factor(house$BsmtCond)
house$BsmtExposure<-factor(house$BsmtExposure)
house$BsmtFinType1<-factor(house$BsmtFinType1)
house$BsmtFinType2<-factor(house$BsmtFinType2)

house$Electrical<-factor(house$Electrical)
house$FireplaceQu<-factor(house$FireplaceQu)
house$GarageType<-factor(house$GarageType)
house$GarageFinish<-factor(house$GarageFinish)

house$GarageQual<-factor(house$GarageQual)
house$GarageCond<-factor(house$GarageCond)
house$PoolQC<-factor(house$PoolQC)
house$Fence<-factor(house$Fence)
house$MiscFeature<-factor(house$MiscFeature)

“`

## Step 6: Transform skewed features

This part aims to determine the skewness of each numeric variable and then transform excessively skewed features with log(x+1)

“`{r skew}
Column_classes <- sapply(names(house),function(x){class(house[[x]])})
numeric_columns <-names(Column_classes[Column_classes != “factor”])

#determining skew of each numeric variable

skew <- sapply(numeric_columns,function(x){skewness(house[[x]],na.rm = T)})

# Let us determine a threshold skewness and transform all variables above the treshold.

skew <- skew[skew > 0.75]

# transform excessively skewed features with log(x + 1)

for(x in names(skew))
{
house[[x]] <- log(house[[x]] + 1)
}
“`
## Step 7: Build models

This part aims to build model on training dataset and then validate the model on validation results via calculating the RMSE to evaluate the model performance.In the end, we use the model to predict house price on the test dataset.

“`{r model}
# Partion datasets into training and validating datasets
set.seed(123)
train <- house[house$isTrain==1,]
test <- house[house$isTrain==0,]

training<-train[sample(seq_len(nrow(train)), size = floor(0.75*nrow(train))), ]
validation<-train[sample(seq_len(nrow(train)), size = floor(0.25*nrow(train))), ]

# Build model
train_model<-randomForest(SalePrice ~ ., training)
# importand variables
importance<-importance(train_model)
varImpPlot(train_model)

# Using validation dataset to calculate RMSE

validation_predict<-predict(train_model, newdata=validation)
RMSE <- function(x,y){
a <- sqrt(sum((log(x)-log(y))^2)/length(y))
return(a)
}
RMSE(validation_predict, validation$SalePrice)
# Prediction on test datasets
rf_predict<-predict(train_model, newdata=test)
submit <- data.frame(Id=test$Id,SalePrice=rf_predict)
write.csv(submit, “C:/Users/212697818/Desktop/submit.csv”)

“`
## 8. Reference
(1) https://www.kaggle.com/c/house-prices-advanced-regression-techniques/discussion/35773
(2) https://www.kaggle.com/kangrinboqeno1/predicting-house-prices-using-r/code

用R Stan设计临床试验

之前曾经做过一个一期和二期A合并的药物试验,用的是continual reassessment method  (CRM)模型取代传统3+3来寻找MTD。具体的CRM用的是一个two-parameter logistic model 来描述dose-response关系,然后给出参数的先验信息和Skeleton信息,再结合数据求出各个剂量的MTD概率,最接近Target概率的那个剂量就是要寻找的最大剂量。听起来很简单,然而实施起来还是有些困难,比如选择什么工具。

之后,两个华人教授(Ji Yuan 和 Yuan Ying (缺失数据大牛RA Little的学生))分别提出了更简单的两个模型:mTPI – Modified Toxicity Probability Interval 和BOIN – Bayesian optimal interval,因为简单易用而且有免费的R程序可用,因而被好多药厂广泛采用寻找一期最大剂量。在快要忘掉CRM时,今天又不经意间在某R包里看到了CRM这三个字。

最近发现了R 里的两个很好用的软件包 – trialr 和 RBesT,是基于R和Stan (一个专门做各种贝叶斯模型的软件,美国统计学家Andrew German团队开发的。还有一个贝叶斯软件也很出名,WinBugs)写的package,非常实用。介绍下这两个Package。

 

trialr里面有几个功能让人振奋:

1.可以设计CRM模型,只需要几行简单的代码就可以实现

target <- 0.25
skeleton <- c(0.05, 0.15, 0.25, 0.4, 0.6)

fit1 <- stan_crm(outcome_str = ‘2NN 3NN 4TT’, skeleton = skeleton,
target = target, model = ’empiric’, beta_sd = sqrt(1.34),
seed = 123)

结果如下:

1.PNG

 

2.可以做hierarchical analysis of response in related cohorts

参见:https://github.com/brockk/trialr

 

RBesT (R Bayesian evidence synthesis Tools)

这是诺华统计团队开发出来的一个R软件包,正如作者Sebastian所言,这个软件包用来利用一切可以利用的信息来设计试验,比如二期试验利用历史信息来降低所需样本量。

Novartis was so kind to grant permission to publish the RBesT (R Bayesian evidence synthesis Tools) R library on CRAN. It’s landed there two days ago. We [Sebastian Weber, Beat Neuenschwander, Heinz Schmidli, Baldur Magnusson, Yue Li, and Satrajit Roychoudhury] have invested a lot of effort into documenting (and testing) that thing properly. So if you follow our vignettes you get an in-depth tutorial into what, how and why we have crafted the library. The main goal is to reduce the sample size in our clinical trials. As such the library performs a meta-analytic-predictive (MAP) analysis using MCMC. Then that MAP prior is turned into a parametric representation, which we usually recommend to “robustify”. That means to add a non-informative mixture component which we put there to ensure that if things go wrong then we still get valid inferences. In fact, robustification is critical when we use this approach to extrapolate from adults to pediatrics. The reason to go parametric is that this makes it much easier to communicate that MAP prior. Moreover, we use conjugate representations such that the library performs operating characteristics with high-precision and high-speed (no more tables of type I error/power, but graphs!). So you see, RBesT does the job for you for the problem to forge a prior and then evaluate it before using it. This library is a huge help for our statisticians at Novartis to apply the robust MAP approach in clinical trials.

作者给了一个诺华二期试验的例子 https://cran.r-project.org/web/packages/RBesT/vignettes/introduction.html

在R里用Stan做临床试验的设计和贝叶斯分析还是很方便的,trialr的作者Kristian Brock很有诚意地提到:

If there is a published Bayesian design you want implemented in Stan, get in touch. Contact brockk on github.

所以如果真遇到什么贝叶斯设计,比如Bayesian group-sequential, bayesian adpative design是Stan里没有的,可以试着联系下 Kristian https://www.birmingham.ac.uk/staff/profiles/cancer-genomic/brock-kristian.aspx

 

想用R和Stan设计临床试验,只需要下载R和Rstudio即可,Stan会嵌套在R里。

下载R: https://mirrors.tuna.tsinghua.edu.cn/CRAN/

下载Rstudio: https://www.rstudio.com/products/rstudio/download/

 

RBesT: https://cran.r-project.org/web/packages/RBesT/vignettes/PoS_codata.html

1000 Oceans


111.jpg
these tears I’ve cried
I’ve cried 1000 oceans
and if it seems I’m floating
in the darkness well
I can’t believe that
I would keep
keep you from flying
and I would cry 1000 more
if that’s what it takes to
sail you home. sail you home.
sail you home.

I’m aware what the rules are
but you know that I will run
you know that I will follow you
over Silbury hill
through the solar field
you know that I will follow you

and if I find you will you still
remember playing at the trains
or does this little blue ball
just fade away

over Silbury hill
though the solar field
you know that I will follow you
I’m aware what the rules are
but you know that I will run
you know that I will follow you

these tears I’ve cried
I’ve cried 1000 oceans
and if it seems I’m floating
in the darkness well
I can’t believe that
I would keep
keep you from flying
so I will cry 1000 more
if that’s what it takes to
sail you home. sail you home.
sail. sail you home.

 

222.jpg

Tori Quotes

[“1000 Oceans”] comes from a few places… It started with a dream I had. An African woman was singing to me singing the melody, humming it to me. I got up and found the piano, got up at 5:45am, recorded the melody and went back to bed.

Inspiration for lyrics came later when her father-in-law died in February 1999. Amos matched the feelings that accompany emotional isolation with her husband’s grieving process. Her husband is Mark Hawley, one of her engineers.

They were so incredibly close that “1000 Oceans” seemed to be the only thing that could bring him out of his sadness. He’d come out and sit and say, “Could you play that one, the ocean song?” It became about feeling close to people you can’t reach, seeing this depth of love for this person who was gone.

What inspired you to write “1000 Oceans”?

Well, different things really. I was woken up in the middle of the night at about 5:30 in the morning or something. And a woman’s voice was singing it to me. She was African, quite ancient. And I couldn’t understand any of the words, but she was humming the first couple phrases. So, I crawled out of bed and found my way to the piano and I put in on a little ghetto blaster so I wouldn’t forget it and in the next few weeks I started to shape it. It wasn’t about one event. It was clear to me that there was this endless determination that the song had to reach her love. And I don’t know if that was a child or a lover or a friend who the song couldn’t seem to be able to make contact with anymore.

So when I finally found, and I was looking through a map and when I look for lyrics I go hunting. So, as I was looking through all the maps and finding places, I was looking on maps of Dartmoor and I was working with a lot of different regions. And finally it hit me, that it was through the solar field and it wasn’t listed on the maps. Because I was being dragged away from the maps to go to sort of a physics book, actually an astronomy book, a book on all sorts of laws and principles of the universe that Marcel had. And, as I finally found solar field it was like I started to feel her jump up and down. Sometimes the songs do that. You get a sense of that they really are alive.

So, my husband had lost his father and he would swing by where I was playing and he would say to me, “Can you play that little song about the oceans?” And that seemed to be sort of a way that we would talk about his dad when no other words could work. So, I think it means different things for different people. But the sense that I got was I couldn’t measure the amount of love that this song had for the person that she was singing it about. And it was quite… it moved me. It was like a resolve, an endless resolve to follow her love. And, she’s not a stalker by the way. [AOL – September 29, 1999]

The last cut on To Venus and Back, “1000 Oceans,” is not only more literal in its lyrical approach than much of the rest of the album, it’s also more musically accessible. The first few bars even conform to a very familiar formula for pop chord progression. Did these aspects of the song surprise you as you were composing?

Sure. An old African woman was humming it to me at 5:30 in the morning in my sleep. I went down to the piano… She was pretty ancient, and I couldn’t understand a word she was saying, so I had to figure the words out. What I kind of got from it was the depth of lust that song had for somebody or something – it could be for this planet, I don’t know. But the idea that this voice that was coming through very clear, finally I understood when I got the phrase “through the solar field.” It all completely aligned, because I knew we were following maps: I was hunting down what the song was trying to tell me.

You know, there’s always galactic reference going on in this record. There’s a scientific vocabulary going on in this record. “Suede” is about seduction, but there’s always a science reference, a physics reference, because that’s the realm of Venus. So, I hung maps all over, and I knew I didn’t have it right, coming up with things. Then finally I got that whatever dimensions the song had to cross to find the being that she was devoted to, whether it was her mother or her sister or her lover or her friend, nothing could stop her. That kind of resilience was a real anchor for the record. [All Music – October 1999]

In old-fashioned terms of craft, the chords on “1000 Oceans” are very solidly constructed. Was there more of a conscious element of songwriting technique on this song as you were writing?

Sure. Well, there’s usually an element of that in all the songs. But there was a moment when I knew that [sings] “I can’t believe that I would keep, keep you from flying.” I kept circling that and circling that, never knowing how I would get out of it. Then to finally go up [sings] “and I would cry a thousand more,” it took me weeks to get there. It was real tortuous to find that, because I didn’t know how I was gonna get past “keep you from flying.” I didn’t know, lyrically or musically. So finally, “and I would cry a thousand more” — and it had to sound like it. She had to have progressed to that; she had to have done that musically. And home [from the next line, “to sail you home”] had to be something. What’s home? Well, home had to be G minor.

What key is the song written in?

B flat. Then to E flat [after the G minor chord], and back to G minor. [All Music – October 1999]

Do you have to feel the emotions of your songs in the real life to be able to describe them?

Sometimes I feel them through other people. Take “1000 Oceans” on To Venus and Back; I had a dream at 5:30 in the morning of an African woman’s voice, excessively tribal. It wasn’t a language I knew. I only understood the melody and it only had a few measures. I went to my piano, in the dark, and recorded 2 measures on a small tape recorder, which is always on the instrument. I sculpted the song during the following weeks, looking at Mark who just lost his father. Until the day when, struck by what I played in the living room, he said, “Could you replay this song about oceans?”

Mark… your husband?

Yes. There was a moment when I realized I saw different pictures than him about this song. I felt a deep love feeling when he came in the song, associating it to the memory and search of his father, because we don’t know where those who leave this earth go. Sometimes, the love feelings of my songs come from someone else and I’m just there to look at it. [Best – October 1999]

In the song there is this ferocious commitment to finding this person. I don’t know who the song is singing about – it’s different for different people when they hear it. She has this depth of love for a daughter or whoever it is. I think some of the other songs look to her sometimes for that kind of resilience. [VH1.com – November 1999]

Some months ago, as I was working on my new double album in Bude in Cornwall, I had a really important dream. A voice appeared in my head. I call hear my dark angel. She was a soul sister, who sang in my head. She was humming a melody to me. It was about half past five or six o’clock in the morning. I got out of bed and went over to the studio. In the country, the people there leave everything unlocked. So the studio was open. I went in and recorded the melody. From this melody the song “1000 Oceans” developed. I worked very long on it until it was finished.

Sometimes it takes an incredible amount of time for me to understand a song I have recorded. Because I am so much in it and I can’t distance myself from it. Sometimes I don’t really understand my songs until I go on tour and live with them. They are like girls for me that keep me accompanied. But sometimes I only understand my songs through the reactions of other people. This was the case with “1000 Oceans.” Mark had just lost his father. The two were very close, because Mark was an only-son. Before the death of my father-in-law they talked on the phone every day. His father fooled us into believing he was getting better. He had cancer and some day he was just dead. It was a shock for Mark, because he had really believed it got better. They had already made plans for his father to come to visit us in the USA. He had never been there. After the old man had died my relationship with Mark got very difficult. He was inconsolable. You can’t do much for somebody who has lost the most important person in his life. I often held him in my arms and made long walks with him. I was just there for him a lot. But I never really got through to him. I still have my mother and my father so I didn’t have the experience. I only reached him with A Thousand Oceans. After Mark heard the song, he always came to me, sat down next to the piano and said, “Please, play that song again.” And I played it to him. Through that we got in contact once again. I took him back from that other galaxy he was in a million miles away from me. So that dream was very special to me. It renewed the connection between Mark and me. [Die Zeit – November 11, 1999]

 

Quotes by Fans

I found out about this song back when I was in high school from my best friend. She was going through a rough situation with a guy she was in love with. She explained to me how she loved this song and how she felt it represented what she felt in regards to her love for this guy that she thought didn’t love her back. I heard this song and instantly feel in love with it. My best friend passed away about a year later and this song has a completely different meaning to me than what it meant to her at that time. Songs have different meanings for different people and the way each person chooses to interpret them into their own life makes the song meaning accurate to that person. 

periwinkle09 on May 16, 2010

  1. https://songmeanings.com/songs/view/13656/
  2. http://www.yessaid.com/lyrics/1999tovenusandback/11_1000oceans.html

How to explicitly specify of mixed models for longitudinal data in trial protocol

(a) the fixed and repeated/random effects

 

(b) the covariance matrix

 

(c) the testing method (e.g, type III F test with the Kenward‐Rogers estimation method for the denominator degrees of freedom or a likelihood ratio test)

 

(d) the computation method (e.g, expectation‐maximization algorithm, Newton‐Raphson algorithm, and Fisher scoring algorithm)
(e) the estimation method (e.g, maximum likelihood, restricted maximum likelihood, estimation or minimum variance quadratic unbiased estimation, and empirical sandwich estimation)

 

(f) the fallback strategy defining the handling of computation or convergence problems

 

an example:

Mean changes from baseline will be analysed using a restricted maximum likelihood (REML)‐based repeated measures approach in combination with the Newton Raphson Algorithm. Analyses will include the fixed, categorical effects of treatment, investigative site, visit, and treatment‐by‐visit interaction, as well as the continuous, fixed covariates of baseline score and baseline score‐by‐visit interaction. A(n) common unstructured (co)variance structure will be used to model the within‐patient errors. If this analysis fails to converge, the following structures will be tested in a subsequent order until model‐convergence is achieved: (insert a list of structures appropriate for the specific application). (…) The Kenward‐Roger approximation will be used to estimate denominator degrees of freedom. Significance tests will be based on least‐squares
means using a two‐sided α = .05 (two‐sided 95% confidence intervals). Analyses will be implemented using (insert software package and analysis procedure). The primary treatment comparisons will be the contrast between treatments at the endpoint visit.

 

Ref:

Sebastian H, Armin K, Florian L. Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols. Pharmaceutical statistics. 2019:4.