Modeling multiple source risk factor data and health outcomes in twins

One challenging feature of some medical research is the existence of multiple sources of exposure information about individual subjects. When an exposure of interest has been measured in a variety of ways, or has been reported on by multiple informants, analysts must decide how best to estimate its association with some interesting outcome. Simply performing a multiple regression analysis of the outcome on all the sources together can be problematic, since those reports are likely to be highly correlated. Alternatively, collapsing the reports into a single measure invariably implies an unfortunate loss of information, and a nagging question as to whether one has done the right thing. Instead, we used Stata 9 to implement a novel application of complex sample survey methods1,2 which allows simultaneous use of multiple reports in single regression model. We further extended the method to accommodate estimation of within- and between-pair effects in twin research. My presentation will use Vietnam era veteran twin data to explore the association between military service in Vietnam with post traumatic stress disorder, and address within-and between-pair effects. We will gently explore how to properly reshape data, derive necessary variables, specify models, and implement Stata’s svy commands to apply the method.