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Re: st: Fw: subgroups in cox regression

From   Steven Samuels <>
Subject   Re: st: Fw: subgroups in cox regression
Date   Sun, 1 May 2011 10:19:45 -0400


Effect modification means that the coefficient the exposure  might differ between stages. To test it, you add an interaction or test for an interaction.  One consequence of  using the -if- statement is that it doesn't isolate a differential effect of your exposure variable, because all components of the model differ: 1) the baseline hazard; 2) the coefficient for race; and 3) the coefficient for your exposure.  This is certainly a possible model and it is a good idea to check for proportionality of the baseline hazard by stage and race by using the strata() option in Stata. I'm also wondering whether you have checked that a linear term in your exposure variable is adequate; you can use the -fracpoly- command to find out.  There is no "lik

// If you have Stata Version < 11 (I presume so, since you use -xi-

xi: stcox  i.race i.stage*igf

// Otherwise with Stata 11
stcox i.race c.igf##i.stage   // add ", robust" to get robust standard error

//The interaction term estimates the difference in igf slopes between the two stages.

// You can get the slopes directly by:

tab stage, gen(stage)  // create indicators stage1 stage2
gen igf1= igf*stage1
gen igf2= igf*stage2
xi: stcox i.race  stage1 igf1 stage2 igf2, nocons   
lincom igf1 - igf2  // estimate and test differences with Wald test


On Apr 30, 2011, at 10:40 PM, catherine duggan wrote:

I have what is probably a basic question, which is currently defeating me.

I have run Cox regression with death as my outcome: my main exposure of interest 

is a a continuous variable (IGF)

I have other covariates in the model - I will just refer to two of them, race (5 

level) and tumor stage (2 level)

Xi: stcox igf i.race i.stage

I did the main analysis, and then did some exploratory subgroup  analysis, with 
the usual caveats of smaller sample sizes and using a  more conservative P value

Xi: stcox igf i.race if stage==1

However a reviewer gave the comment

' what you might want to do is to construct a model for effect  modification - 
that is compare the model with no subgroup to one with  trends fit for each 
subgroup - a 1 dgree of freedom likelihood ratio  test - do see if the trends 
differ statistically - a test for  homogeneity of trends.  This would be more 

Unfortunately I have no idea how to accomplish this - I wasn't sure whether this 

was a test for interaction or not.

I'd appreciate any guidance

Many thanks

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