VARIANTS OF DOUBLE ROBUST ESTIMATORS FOR TWO-STAGE DYNAMIC TREATMENT REGIMES

Abstract

Certain conditions and illnesses may necessitate multiple stages of treatment and thus require
unique study designs to compare the efficacy of these interventions. Such studies are
characterized by two or more stages of treatment punctuated by decision points where intermediate
outcomes inform the choice for the next stage of treatment. The algorithm that
dictates what treatments to take based on intermediate outcomes is referred to as a dynamic
regime. This paper describes an efficient method of building double robust estimators of
the treatment effect of different regimes. A double robust estimator utilizes both modeling
of the outcome and weighting based on the modeled probability of receiving treatment in
such a way that the resulting estimator is a consistent estimate of the desired population
parameter under the condition that at least one of those models is correct. This new and
more efficient double robust estimator is compared to another double robust estimator as
well as classical regression and inverse probability weighted estimators. The methods are
applied to estimate the regime effects in the STAR*D anti-depression treatment trial.