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There has been significant attention given to developing data-driven methodsfor tailoring patient care based on individual patient characteristics. Dynamictreatment regimes formalize this through a sequence of decision rules that mappatient information to a suggested treatment. The data for estimating andevaluating treatment regimes are ideally gathered through the use of SequentialMultiple Assignment Randomized Trials (SMARTs) though longitudinalobservational studies are commonly used due to the potentially prohibitivecosts of conducting a SMART. These studies are typically sized for simplecomparisons of fixed treatment sequences or, in the case of observationalstudies, a priori sample size calculations are often not performed. We developsample size procedures for the estimation of dynamic treatment regimes fromobservational studies. Our approach uses pilot data to ensure a study will havesufficient power for comparing the value of the optimal regime, i.e. theexpected outcome if all patients in the population were treated by followingthe optimal regime, with a known comparison mean. Our approach also ensures thevalue of the estimated optimal treatment regime is within an a priori set rangeof the value of the true optimal regime with a high probability. We examine theperformance of the proposed procedure with a simulation study and use it tosize a study for reducing depressive symptoms using data from electronic healthrecords.

Using Pilot Data to Size Observational Studies for the Estimation of Dynamic Treatment Regimes