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From |
Steve Samuels <[email protected]> |

To |
[email protected] |

Subject |
Re: st: GEE and planned missingness |

Date |
Tue, 25 Jun 2013 19:00:38 -0400 |

I'm going to reduce your problem to two variables not measured at every wave plus a third measured every time. Suppose x is measured at waves 1-3; u is measured at waves 3-4; z is measured at all waves . gen x1 = x*(wave == 1) . gen x2 = x*(wave == 2 | wave==3) . gen u1 = u*(wave ==4) . gen u2 = u*(wave ==2 | wave ==3) These are defined for all waves The following model will fit everything . xtgee mjuser x1 x2 u1 u2 i.wave z To test for a differential effect of x between wave 1 and waves 2 and 3 . lincom _b[x1] - _b[x2] You might expect such an interaction, because collinearity of u and x will change the meaning of the coefficient _b[x2]. To check this, fit a model with x1 and x2 only, ignoring wave 4; and u1 and u2, ignoring wave 1. In fact, I'd recommend that you do this first and test for differential wave effects on x and u. Another approach would be to multiply impute x and u in the wave where each is missing. A completely different question: is this survey data? If so, I suggest that you -svyset- and use -svy: glm- with one of the approaches above; you won't need id information, since tests, SEs, and CIs will be based on the survey design factors. Steve [email protected] . To fit all the data at once, you create interactions of X1 with group and X2 with group, letting group 2 (waves 2 and 3) be the baseline: Then: On Jun 25, 2013, at 12:10 PM, Kris Anderson wrote: Hi, all. I am running GEE with panel data with four waves of data. I have time varying predictors that were assessed at different time points (i.e., predictors at wave 1-3 [NMU] and a different set at waves 2-4 [CC EXPAND SE]). I'd like to include them in the analysis but my examination of the documentation (and past posts) suggests that cases with missingness at a given time point will be dropped. Given that data was missing by design for all cases at these waves, this is problematic. Is there a way around this? Here's my code for reference. xtgee mjuser CC EXPAN NMU SE sexR i.wave, f(binomial) corr(uns) vce(robust) eform estat wcorr I'd appreciate suggestions of alternatives if this is a nonstarter. Thanks, Kris * * 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/ On Jun 25, 2013, at 12:10 PM, Kris Anderson wrote: Hi, all. I am running GEE with panel data with four waves of data. I have time varying predictors that were assessed at different time points (i.e., predictors at wave 1-3 [NMU] and a different set at waves 2-4 [CC EXPAND SE]). I'd like to include them in the analysis but my examination of the documentation (and past posts) suggests that cases with missingness at a given time point will be dropped. Given that data was missing by design for all cases at these waves, this is problematic. Is there a way around this? Here's my code for reference. xtgee mjuser CC EXPAN NMU SE sexR i.wave, f(binomial) corr(uns) vce(robust) eform estat wcorr I'd appreciate suggestions of alternatives if this is a nonstarter. Thanks, Kris * * 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/ * * 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/

**References**:**st: GEE and planned missingness***From:*Kris Anderson <[email protected]>

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