|Title||Sizing a trial for estimation of an optimal treatment regime|
|Year of Publication||2014|
|Authors||Laber, Eric B.|
A personalized treatment strategy is a rule that uses individual patient information to form treatment recommendations. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials with the primary aim of estimating a personalized treatment strategy. However, at present there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small changes in the data, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a two-armed randomized clinical trial when the primary aim is estimating the optimal personalized treatment strategy. The proposed method is based on inverting a plug-in projection confidence interval and is thereby regular and robust to small changes in the data. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness.
Sizing a trial for estimation of an optimal treatment regime