Re: [R] complex contrasts and logistic regression
Hi, Sorry to take so long to reply, I was travelling last week. Thanks for your suggestions. Actually in this case contrast and predict gave the same result, and what I was looking at was the correct odds from the model. What is still confusing me is the 1st part of my question, looking for a trend in odds ratios. From what I understand testing the interaction: fit1-glmD(survived ~ as.numeric(Covariate)+Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) fit2-glmD(survived ~ as.numeric(Covariate)*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) lrtest(fit1,fit2) Would be effectively testing for a trend in odds ratios? Do I have to fiddle with contrasts to make sure I am testing the correct parameter? Thanks Nicholas On Sat, 16 Jun 2007 11:14:12 -0500, Frank E Harrell Jr [EMAIL PROTECTED] said: Nicholas Lewin-Koh wrote: Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) For logistic regression you can also use Design's lrm function which gives you more options. My covariate has three levels (A,B and C) and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to treatment in the trial, but Covariate is something that is measured posthoc and can vary in the population. If by posthoc you mean that the covariate is measured after baseline, it is difficult to get an interpretable analysis. I am trying to wrap my head around how to calculate a few quantities from the model and get reasonable confidence intervals for them, namely I would like to test H0: gamma=0, where gamma is the regression coefficient of the odds ratios of surviving under treatment vs control at each level of Covariate (adjusted for the confounder) You mean regression coefficient on the log odds ratio scale. This is easy to do with the contrast( ) function in Design. Do ?contrast.Design for details and examples. and I would like to get the odds of surviving at each level of Covariate under treatment and control for each level of covariate adjusted for the confounder. I have looked at contrast in the Design library but I don't think it gives me the right quantity, for instance contrast(fit,list(covariate=A, Therapy=Treated, confounder=median(myDat$confounder), X=TRUE) ( A is the baseline level of Covariate) gives me beta0 + beta_Treated + beta_confounder*68 Is this correctly interpreted as the conditional odds of dying? As to the 1st contrast I am not sure how to get it, would it be using type = 'average' with some weights in contrast? The answers are probably staring me in the face, i am just not seeing them today. contrast( ) is for contrasts (differences). Sounds like you want predicted values. Do ?predict ?predict.lrm ?predict.Design. Also do ?gendata which will generate a data frame for getting predictors, with unspecified predictors set to reference values such as medians. Frank Nicholas -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@stat.math.ethz.ch 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] complex contrasts and logistic regression
Nicholas Lewin-Koh wrote: Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) For logistic regression you can also use Design's lrm function which gives you more options. My covariate has three levels (A,B and C) and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to treatment in the trial, but Covariate is something that is measured posthoc and can vary in the population. If by posthoc you mean that the covariate is measured after baseline, it is difficult to get an interpretable analysis. I am trying to wrap my head around how to calculate a few quantities from the model and get reasonable confidence intervals for them, namely I would like to test H0: gamma=0, where gamma is the regression coefficient of the odds ratios of surviving under treatment vs control at each level of Covariate (adjusted for the confounder) You mean regression coefficient on the log odds ratio scale. This is easy to do with the contrast( ) function in Design. Do ?contrast.Design for details and examples. and I would like to get the odds of surviving at each level of Covariate under treatment and control for each level of covariate adjusted for the confounder. I have looked at contrast in the Design library but I don't think it gives me the right quantity, for instance contrast(fit,list(covariate=A, Therapy=Treated, confounder=median(myDat$confounder), X=TRUE) ( A is the baseline level of Covariate) gives me beta0 + beta_Treated + beta_confounder*68 Is this correctly interpreted as the conditional odds of dying? As to the 1st contrast I am not sure how to get it, would it be using type = 'average' with some weights in contrast? The answers are probably staring me in the face, i am just not seeing them today. contrast( ) is for contrasts (differences). Sounds like you want predicted values. Do ?predict ?predict.lrm ?predict.Design. Also do ?gendata which will generate a data frame for getting predictors, with unspecified predictors set to reference values such as medians. Frank Nicholas -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@stat.math.ethz.ch 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.
[R] complex contrasts and logistic regression
Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels (A,B and C) and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to treatment in the trial, but Covariate is something that is measured posthoc and can vary in the population. I am trying to wrap my head around how to calculate a few quantities from the model and get reasonable confidence intervals for them, namely I would like to test H0: gamma=0, where gamma is the regression coefficient of the odds ratios of surviving under treatment vs control at each level of Covariate (adjusted for the confounder) and I would like to get the odds of surviving at each level of Covariate under treatment and control for each level of covariate adjusted for the confounder. I have looked at contrast in the Design library but I don't think it gives me the right quantity, for instance contrast(fit,list(covariate=A, Therapy=Treated, confounder=median(myDat$confounder), X=TRUE) ( A is the baseline level of Covariate) gives me beta0 + beta_Treated + beta_confounder*68 Is this correctly interpreted as the conditional odds of dying? As to the 1st contrast I am not sure how to get it, would it be using type = 'average' with some weights in contrast? The answers are probably staring me in the face, i am just not seeing them today. Nicholas __ R-help@stat.math.ethz.ch 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.
