Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials.

ABSTRACT: Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual

SUBMITTER: Diaz FJ 

PROVIDER: S-EPMC10773237 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature