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From | Steve Samuels <[email protected]> |
To | [email protected] |
Subject | Re: st: Test for effect modification/interaction using svy |
Date | Mon, 3 Feb 2014 18:33:16 -0500 |
The first command, with i.vdpcat#excessVA is incorrect because it does not separate main effects from interactions; instead it fit a separate parameter for each of the 2 x 3 combinations of the two terms. You would have seen this if you had examined the results. The second formulation with i.vcpcat##excessVA creates the main effect terms automatically and so is the correct equation. (as would be the command with main effects & interactions specified individually "i.vdpcat excessVA i.vdpcat#excessVA") As a result, the 3 d.f. test for interaction generated by . testparm i.vdpcat#exbloodVA is correct. If you are still unclear about interactions, "search interaction, all" will yield many resource. Steve [email protected] > On Jan 31, 2014, at 7:27 AM, Schmutz Einat <[email protected]> wrote: > > Dear all > > > > I am trying to statistically compare two cox regression models (nested) using svy commands to see whether effect modification exists. What I did is I included an interaction term (2 categorical variables) and run the adjusted Wald test (as postestimation commands normally used after stcox, such as lrtest, don't work with svy). > > > > Syntax for the two models I want to compare („i.vdpcat“ is the exposure variable, “var1-4” are confounding variables and “excessVA” is the potential effect modifier): > > > > svyset [w=weightvar], psu(psuvar) strata(straatavar) vce(linearized) > stset timevar, failure (failvar) > > svy, subpop(if ...): stcox var1 var2 i.var3 i.var4 i.vdpcat > > svy, subpop(if ...): stcox var1 var2 i.var3 i.var4 i.vdpcat#excessVA > > > testparm i.vdpcat#excessVA > > > What I get is: > ---------------------------------------------------------------------------------- > | Linearized > _t | Haz. Ratio Std. Err. t P>|t| [95% Conf. Interval] > -----------------+---------------------------------------------------------------- > ... > ... > vdpcat#exbloodVA | > 1 2 | 1.170165 .1309874 1.40 0.167 .9344425 1.465352 > 2 1 | .9572346 .0975444 -0.43 0.670 .7799799 1.174771 > 2 2 | .9208137 .1008386 -0.75 0.455 .7389192 1.147484 > 3 1 | .8220665 .0850683 -1.89 0.064 .6677197 1.012092 > 3 2 | .9301641 .0882042 -0.76 0.449 .7687764 1.125432 > 4 1 | .8522502 .0738444 -1.85 0.071 .7160554 1.014349 > 4 2 | .8321073 .0976341 -1.57 0.124 .6573199 1.053372 > ---------------------------------------------------------------------------------- > > . testparm i.vdpcat#exbloodVA > > Adjusted Wald test > > ( 1) 1b.vdpcat#2.exbloodVA = 0 > ( 2) 2.vdpcat#1b.exbloodVA = 0 > ( 3) 2.vdpcat#2.exbloodVA = 0 > ( 4) 3.vdpcat#1b.exbloodVA = 0 > ( 5) 3.vdpcat#2.exbloodVA = 0 > ( 6) 4.vdpcat#1b.exbloodVA = 0 > ( 7) 4.vdpcat#2.exbloodVA = 0 > > F( 7, 43) = 3.12 > Prob > F = 0.0094 > > What I understand is that there is a statistically significant difference in survival among the 8 groups. Now, can I conclude that, since the Wald test is significant (p≤0.05), there is an interaction between vdpcat and exbloodVA and that the second model (including the interaction variable) is the better/more accurate model in predicting my outcome (survival)? > > In addition, what does the following test (using ##) tell me? Is this the accurate way to test a possible interaction between vdpcat and excessVA? > > svy, subpop(if ...): stcox var1 var2 i.var3 i.var4 i.vdpcat##excessVA > > testparm i.vdpcat#excessVA > > > ---------------------------------------------------------------------------------- > | Linearized > _t | Haz. Ratio Std. Err. t P>|t| [95% Conf. Interval] > -----------------+---------------------------------------------------------------- > ... > ... > 2.exbloodVA | 1.170165 .1309874 1.40 0.167 .9344425 1.465352 > | > vdpcat | > 2 | .9572346 .0975444 -0.43 0.670 .7799799 1.174771 > 3 | .8220665 .0850683 -1.89 0.064 .6677197 1.012092 > 4 | .8522502 .0738444 -1.85 0.071 .7160554 1.014349 > | > exbloodVA#vdpcat | > 2 2 | .822065 .1190204 -1.35 0.182 .6145367 1.099675 > 2 3 | .9669529 .157167 -0.21 0.837 .6975098 1.34048 > 2 4 | .8343821 .1049943 -1.44 0.157 .6479522 1.074452 > ---------------------------------------------------------------------------------- > > . testparm i.vdpcat#exbloodVA > > Adjusted Wald test > > ( 1) 2.exbloodVA#2.vdpcat = 0 > ( 2) 2.exbloodVA#3.vdpcat = 0 > ( 3) 2.exbloodVA#4.vdpcat = 0 > > F( 3, 47) = 1.11 > Prob > F = 0.3540 > > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/