Biostatistics Weekly Seminar

Bias due to unmeasured confounding is a common concern when researchers estimate a treatment effect using observational data. To address this concern, instrumental variable methods, such as two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI), have been widely adopted. In many clinical studies with survival outcomes, 2SRI has been accepted as the method of choice over 2SPS, but a compelling theoretical rationale has not been postulated. We evaluate the bias and consistency in estimating the conditional treatment effect for both 2SPS and 2SRI when the outcome is binary, count, or time to event. We demonstrate analytically that the bias in 2SPS and 2SRI estimators can be reframed to mirror the problem of omitted variables in nonlinear models and that there is a direct relationship with the collapsibility of effect measures. In contrast to conclusions made by previous studies, we demonstrate that the consistency of 2SRI estimators only holds under the following conditions: (1) when the null hypothesis is true; (2) when the outcome model is collapsible; or (3) under the strong and unrealistic assumption that the effect of the unmeasured covariates on the treatment is proportional to their effect on the outcome. We propose a novel dissimilarity metric to provide an intuitive explanation of the bias of 2SRI estimators in noncollapsible models and demonstrate that with increasing dissimilarity, the bias of 2SRI increases in magnitude. Motivating examples from the clinical literature on treatments of kidney cancer will be presented.

MRBIII, Room 1220
20 February 2019
1:30pm