G-estimation of the effect of exposures in longitudinal studies
Stata's st suite of commands for the analysis of survival time data allow flexible modeling of the effect of exposures which vary over time. A potential problem in such analyses is that other risk factors may be both confounders (i.e., associated with both exposure and disease outcome) and also intermediate variables (on the causal pathway from exposure to disease). This phenomenon is known as "time-varying confounding". Standard statistical models for the analysis of cohort studies do not take such complex relationships into account and may produce biased estimates of the effect of risk factor changes. G-estimation of the effect of a time-varying exposure on outcome, allowing for confounders which are also on the causal pathway, has been proposed for the analysis of such inter-related data. We will present stgest, a Stata program which performs g-estimation, allowing the results to be compared to those from the more usual survival analysis. Using simulated data, we show that the usual analysis can under-estimate the effect of an exposure on disease where there is time-varying confounding, and that g-estimation produces a more accurate estimate. Applications of the method will be discussed.
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