Identifying important explanatory variables for time-varying outcomes.

Oliver Bembom, University of California, Berkeley
Maya L. Petersen, University of California, Berkeley
Mark J. van der Laan, University of California, Berkeley

Abstract

This chapter describes a systematic and targeted approach for estimating the impact of each of a large number of baseline covariates on an outcome that is measured repeatedly over time. These variable importance estimates can be adjusted for a user-specified set of confounders and lend themselves in a straightforward way to obtaining confidence intervals and p-values. Hence, they can in particular be used to identify a subset of baseline covariates that are the most important explanatory variables for the time-varying outcome of interest. We illustrate the methodology in a data analysis aimed at finding mutations of the human immunodeficiency virus that predict how well a patient responds to a drug regimen containing the two antiretroviral drugs lamivudine and stavudine. The most significant mutation we identify, 184IV, has previously been characterized as conferring high-level resistance to lamivudine. Our analysis furthermore points to a second mutation, 75AIMTS, that has been linked to moderate resistance to both lamivudine and stavudine.