|Title||Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Laber, Eric B., Ying-Qi Zhao, Todd Regh, Marie Davidian, Anastasios Tsiatis, Joseph B. Stanford, Donglin Zeng, Rui Song, and Michael R. Kosorok|
|Date Published||2016 Apr 15|
|Keywords||Biostatistics, Computer Simulation, Confidence Intervals, Data Interpretation, Statistical, Evidence-Based Practice, Female, Fertility, Humans, Male, Models, Statistical, Pilot Projects, Precision Medicine, Pregnancy, Randomized Controlled Trials as Topic, Regression Analysis, Sample Size|
A personalized treatment strategy formalizes evidence-based treatment selection by mapping patient information to a recommended treatment. 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 when one of the primary aims is estimation of 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 perturbations in the underlying generative model, 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 single stage, two-armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness.
|Alternate Journal||Stat Med|
|Original Publication||Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.|
|PubMed Central ID||PMC4777666|
|Grant List||K23 HD001479 / HD / NICHD NIH HHS / United States |
P01 CA142538 / CA / NCI NIH HHS / United States
R01 AA023187 / AA / NIAAA NIH HHS / United States
U10 CA180819 / CA / NCI NIH HHS / United States
Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.