Re: [R] How do I specify a partially completed survival analysis model?
On Nov 23, 2009, at 12:50 PM, David Winsemius wrote: On Nov 20, 2009, at 1:27 PM, David Winsemius wrote: On Nov 20, 2009, at 11:07 AM, RWilliam wrote: In reply to suggestion by David W., setting an offset parameter doesn't seem to work as R is not recognizing the "X2" part of coxph( Surv(Time,Censor)~X1, offset=log(4.3*X2), data= a ). Also, here's some sample data: The problem, arising as a result of not having a dataset against which to test my memories of syntactic niceties, is that glm and coxph use different methods of supplying offsets. It's been pointed out to me that coxph()'s required syntactic incorporation of offsets is the same as glm()'s preferred inclusion in the formula, and that my erroneous impression that a separate offset argument is necessary might have be the result of "SAS poisoning". I suspect that "infection" is the more correct biomedical analogy, since I copied my use from another who was probably the index case. That usage was also similar to the separate specification of offsets (e.g. $CAL LPY=%LOG(PY) $OFFSET LPY) in GLIM which was my statistical upbringing. Would that be SAS1N1 and is there a vaccine that one can distribute to universities and corporations to prevent the spread of the infection? ;-) Regards, Marc Schwartz __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How do I specify a partially completed survival analysis model?
On Nov 20, 2009, at 1:27 PM, David Winsemius wrote: On Nov 20, 2009, at 11:07 AM, RWilliam wrote: In reply to suggestion by David W., setting an offset parameter doesn't seem to work as R is not recognizing the "X2" part of coxph( Surv(Time,Censor)~X1, offset=log(4.3*X2), data= a ). Also, here's some sample data: The problem, arising as a result of not having a dataset against which to test my memories of syntactic niceties, is that glm and coxph use different methods of supplying offsets. It's been pointed out to me that coxph()'s required syntactic incorporation of offsets is the same as glm()'s preferred inclusion in the formula, and that my erroneous impression that a separate offset argument is necessary might have be the result of "SAS poisoning". I suspect that "infection" is the more correct biomedical analogy, since I copied my use from another who was probably the index case. That usage was also similar to the separate specification of offsets (e.g. $CAL LPY=%LOG(PY) $OFFSET LPY) in GLIM which was my statistical upbringing. -- David. Thereau and Gramsch's book has examples, but if you did not have the book you still had alternatives. A bit of searching with the terms: coxph Therneau offset; produced lots of hits for the occurrence of offset in warning messages so adding -warning to that search then produced a hit to the Google books look at T&G's text with a worked example: > a$logX2 <- log(a$X2) > coxph(Surv(Time,Censor)~X1 + offset(logX2), data= a ) Call: coxph(formula = Surv(Time, Censor) ~ X1 + offset(logX2), data = a) coef exp(coef) se(coef) zp X1 -0.885 0.413 1.43 -0.62 0.54 #Or just: > coxph(Surv(Time,Censor)~X1 + offset(log(4.3*X2)), data= a ) X1 X2 TimeCensor 1 1 0.40619454 77.00666 0 2 1 0.20717868 100.0 0 3 1 0.77360963 79.03463 1 4 1 0.62221954 100.0 0 5 1 0.32191280 100.0 0 6 1 0.73790704 72.84842 0 7 1 0.65012237 100.0 0 8 1 0.71596105 100.0 0 9 1 0.74787202 84.00172 0 10 1 0.66803790 41.65760 0 11 1 0.79922364 92.41999 0 12 1 0.76433736 90.99983 0 13 1 0.57014524 100.0 0 14 1 0.39642235 100.0 0 15 1 0.55756045 100.0 0 16 0 0.60079340 100.0 0 17 0 0.43630695 100.0 0 18 0 0.09388013 100.0 0 19 0 0.55956791 100.0 0 20 0 0.52491597 97.71884 1 where we set the coefficient of X2 to be 8. RWilliam wrote: Sorry for being impatient but is there really no way of doing this at all? It's quite urgent so any help is very much appreciated. Thank you. RWilliam wrote: Hello, I just started using R to do epidemiologic simulation research using the Cox proportional hazard model. I have 2 covariates X1 and X2 which I want to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence of X from t. After I simulate Time and Censor data vectors denoting the censoring time and status respectively, I can call the following function to fit the data into the Cox model (a is a data.frame containing 4 columns X1, X2, Time and Censor): b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); Now the purpose of me doing simulation is that I have another mechanism to generate the number b2. From the given b2 (say it's 4.3), Cox model can be fit to generate b1 and check how feasible the new model is. Thus, my question is, how do I specify such a model that is partially completed (as in b2 is known). I tried things like Surv(Time,Censor)~X1+4.3*X2, but it's not working. Thanks very much. -- View this message in context: http://old.nabble.com/How-do-I-specify-a-partially-completed-survival-analysis-model--tp26421391p26443562.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius, MD Heritage Laboratories West Hartford, CT __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius, MD Heritage Laboratories West Hartford, CT __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How do I specify a partially completed survival analysis model
--- begin inclusion -- After I simulate Time and Censor data vectors denoting the censoring time and status respectively, I can call the following function to fit the data into the Cox model (a is a data.frame containing 4 columns X1, X2, Time and Censor): b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); Now the purpose of me doing simulation is that I have another mechanism to generate the number b2. From the given b2 (say it's 4.3), Cox model can be fit to generate b1 and check how feasible the new model is. Thus, my question is, how do I specify such a model that is partially completed (as in b2 is known). I tried things like Surv(Time,Censor)~X1+4.3*X2, but it's not working. Thanks very much. ---end inclusion 1. Use an offset argument. Anything therein is put into the linear predictor "as is". coxph(Surv(Time, Censor) ~ X1 + offset(X2*b2), data=a) 2. method='breslow' This never ceases to amaze me. The Efron approximation is uniformly superior to the Breslow -- that's why it is the default --- but the inferior method remains more popular to the point that people force the program to use it. I suppose because it was easier to program and thus was the first one implimented. However, for simulated data there will not be any ties in "time" so the two are identical. Terry Therneau __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How do I specify a partially completed survival analysis model?
On Nov 20, 2009, at 11:07 AM, RWilliam wrote: In reply to suggestion by David W., setting an offset parameter doesn't seem to work as R is not recognizing the "X2" part of coxph( Surv(Time,Censor)~X1, offset=log(4.3*X2), data= a ). Also, here's some sample data: The problem, arising as a result of not having a dataset against which to test my memories of syntactic niceties, is that glm and coxph use different methods of supplying offsets. Thereau and Gramsch's book has examples, but if you did not have the book you still had alternatives. A bit of searching with the terms: coxph Therneau offset; produced lots of hits for the occurrence of offset in warning messages so adding -warning to that search then produced a hit to the Google books look at T&G's text with a worked example: > a$logX2 <- log(a$X2) > coxph(Surv(Time,Censor)~X1 + offset(logX2), data= a ) Call: coxph(formula = Surv(Time, Censor) ~ X1 + offset(logX2), data = a) coef exp(coef) se(coef) zp X1 -0.885 0.413 1.43 -0.62 0.54 #Or just: > coxph(Surv(Time,Censor)~X1 + offset(log(4.3*X2)), data= a ) X1 X2 TimeCensor 1 1 0.40619454 77.00666 0 2 1 0.20717868 100.0 0 3 1 0.77360963 79.03463 1 4 1 0.62221954 100.0 0 5 1 0.32191280 100.0 0 6 1 0.73790704 72.84842 0 7 1 0.65012237 100.0 0 8 1 0.71596105 100.0 0 9 1 0.74787202 84.00172 0 10 1 0.66803790 41.65760 0 11 1 0.79922364 92.41999 0 12 1 0.76433736 90.99983 0 13 1 0.57014524 100.0 0 14 1 0.39642235 100.0 0 15 1 0.55756045 100.0 0 16 0 0.60079340 100.0 0 17 0 0.43630695 100.0 0 18 0 0.09388013 100.0 0 19 0 0.55956791 100.0 0 20 0 0.52491597 97.71884 1 where we set the coefficient of X2 to be 8. RWilliam wrote: Sorry for being impatient but is there really no way of doing this at all? It's quite urgent so any help is very much appreciated. Thank you. RWilliam wrote: Hello, I just started using R to do epidemiologic simulation research using the Cox proportional hazard model. I have 2 covariates X1 and X2 which I want to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence of X from t. After I simulate Time and Censor data vectors denoting the censoring time and status respectively, I can call the following function to fit the data into the Cox model (a is a data.frame containing 4 columns X1, X2, Time and Censor): b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); Now the purpose of me doing simulation is that I have another mechanism to generate the number b2. From the given b2 (say it's 4.3), Cox model can be fit to generate b1 and check how feasible the new model is. Thus, my question is, how do I specify such a model that is partially completed (as in b2 is known). I tried things like Surv(Time,Censor)~X1+4.3*X2, but it's not working. Thanks very much. -- View this message in context: http://old.nabble.com/How-do-I-specify-a-partially-completed-survival-analysis-model--tp26421391p26443562.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius, MD Heritage Laboratories West Hartford, CT __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How do I specify a partially completed survival analysis model?
In reply to suggestion by David W., setting an offset parameter doesn't seem to work as R is not recognizing the "X2" part of coxph( Surv(Time,Censor)~X1, offset=log(4.3*X2), data= a ). Also, here's some sample data: X1 X2 TimeCensor 1 1 0.40619454 77.00666 0 2 1 0.20717868 100.0 0 3 1 0.77360963 79.03463 1 4 1 0.62221954 100.0 0 5 1 0.32191280 100.0 0 6 1 0.73790704 72.84842 0 7 1 0.65012237 100.0 0 8 1 0.71596105 100.0 0 9 1 0.74787202 84.00172 0 10 1 0.66803790 41.65760 0 11 1 0.79922364 92.41999 0 12 1 0.76433736 90.99983 0 13 1 0.57014524 100.0 0 14 1 0.39642235 100.0 0 15 1 0.55756045 100.0 0 16 0 0.60079340 100.0 0 17 0 0.43630695 100.0 0 18 0 0.09388013 100.0 0 19 0 0.55956791 100.0 0 20 0 0.52491597 97.71884 1 where we set the coefficient of X2 to be 8. RWilliam wrote: > > Sorry for being impatient but is there really no way of doing this at all? > It's quite urgent so any help is very much appreciated. Thank you. > > > > RWilliam wrote: >> >> Hello, >> >> I just started using R to do epidemiologic simulation research using the >> Cox proportional hazard model. I have 2 covariates X1 and X2 which I want >> to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence of X >> from t. >> >> After I simulate Time and Censor data vectors denoting the censoring time >> and status respectively, I can call the following function to fit the >> data into the Cox model (a is a data.frame containing 4 columns X1, X2, >> Time and Censor): >> b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); >> >> Now the purpose of me doing simulation is that I have another mechanism >> to generate the number b2. From the given b2 (say it's 4.3), Cox model >> can be fit to generate b1 and check how feasible the new model is. Thus, >> my question is, how do I specify such a model that is partially completed >> (as in b2 is known). I tried things like Surv(Time,Censor)~X1+4.3*X2, but >> it's not working. Thanks very much. >> > > -- View this message in context: http://old.nabble.com/How-do-I-specify-a-partially-completed-survival-analysis-model--tp26421391p26443562.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How do I specify a partially completed survival analysis model?
On Nov 20, 2009, at 9:57 AM, David Winsemius wrote: On Nov 20, 2009, at 9:46 AM, RWilliam wrote: Sorry for being impatient but is there really no way of doing this at all? It's quite urgent so any help is very much appreciated. Thank you. The general method with glm's to specify a model with fixed coefficients is to use an offset. I believe that the coxph function also has that facility and seem to remember that Therneau uses offsets in some of the examples he offers in his books and technical reports. Perhaps: cmod <- coxph( Surv(Time,Censor)~X1, offset=4.3*X2, data= ) Or much more likely: cmod <- coxph( Surv(Time,Censor)~X1, offset=log(4.3*X2), data= ) I forgot what scale I should be thinking on. Sorry. -- David Further requests about specifics should be accompanied (as suggested by the Posting Guide) by some code that sets up a reproducible example. -- David. RWilliam wrote: Hello, I just started using R to do epidemiologic simulation research using the Cox proportional hazard model. I have 2 covariates X1 and X2 which I want to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence of X from t. After I simulate Time and Censor data vectors denoting the censoring time and status respectively, I can call the following function to fit the data into the Cox model (a is a data.frame containing 4 columns X1, X2, Time and Censor): b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); Now the purpose of me doing simulation is that I have another mechanism to generate the number b2. From the given b2 (say it's 4.3), Cox model can be fit to generate b1 and check how feasible the new model is. Thus, my question is, how do I specify such a model that is partially completed (as in b2 is known). I tried things like Surv(Time,Censor)~X1+4.3*X2, but it's not working. Thanks very much. -- View this message in context: http://old.nabble.com/How-do-I-specify-a-partially-completed-survival-analysis-model--tp26421391p26441878.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius, MD Heritage Laboratories West Hartford, CT __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius, MD Heritage Laboratories West Hartford, CT __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How do I specify a partially completed survival analysis model?
On Nov 20, 2009, at 9:46 AM, RWilliam wrote: Sorry for being impatient but is there really no way of doing this at all? It's quite urgent so any help is very much appreciated. Thank you. The general method with glm's to specify a model with fixed coefficients is to use an offset. I believe that the coxph function also has that facility and seem to remember that Therneau uses offsets in some of the examples he offers in his books and technical reports. Perhaps: cmod <- coxph( Surv(Time,Censor)~X1, offset=4.3*X2, data= ) Further requests about specifics should be accompanied (as suggested by the Posting Guide) by some code that sets up a reproducible example. -- David. RWilliam wrote: Hello, I just started using R to do epidemiologic simulation research using the Cox proportional hazard model. I have 2 covariates X1 and X2 which I want to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence of X from t. After I simulate Time and Censor data vectors denoting the censoring time and status respectively, I can call the following function to fit the data into the Cox model (a is a data.frame containing 4 columns X1, X2, Time and Censor): b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); Now the purpose of me doing simulation is that I have another mechanism to generate the number b2. From the given b2 (say it's 4.3), Cox model can be fit to generate b1 and check how feasible the new model is. Thus, my question is, how do I specify such a model that is partially completed (as in b2 is known). I tried things like Surv(Time,Censor)~X1+4.3*X2, but it's not working. Thanks very much. -- View this message in context: http://old.nabble.com/How-do-I-specify-a-partially-completed-survival-analysis-model--tp26421391p26441878.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius, MD Heritage Laboratories West Hartford, CT __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How do I specify a partially completed survival analysis model?
Sorry for being impatient but is there really no way of doing this at all? It's quite urgent so any help is very much appreciated. Thank you. RWilliam wrote: > > Hello, > > I just started using R to do epidemiologic simulation research using the > Cox proportional hazard model. I have 2 covariates X1 and X2 which I want > to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence of X from > t. > > After I simulate Time and Censor data vectors denoting the censoring time > and status respectively, I can call the following function to fit the data > into the Cox model (a is a data.frame containing 4 columns X1, X2, Time > and Censor): > b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method = "breslow"); > > Now the purpose of me doing simulation is that I have another mechanism to > generate the number b2. From the given b2 (say it's 4.3), Cox model can be > fit to generate b1 and check how feasible the new model is. Thus, my > question is, how do I specify such a model that is partially completed (as > in b2 is known). I tried things like Surv(Time,Censor)~X1+4.3*X2, but it's > not working. Thanks very much. > -- View this message in context: http://old.nabble.com/How-do-I-specify-a-partially-completed-survival-analysis-model--tp26421391p26441878.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
