Personalized treatment selection using data from crossover designs with carry-over effects.

ABSTRACT: In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry-over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry-over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient

SUBMITTER: Siriwardhana C 

PROVIDER: S-EPMC6842056 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature