A SuperLearner Approach to Predict Run-In Selection in Clinical Trials.

ABSTRACT: A critical early step in a clinical trial is defining the study sample that appropriately represents the target population from which the sample will be drawn. Envisaging a "run-in" process in study design may accomplish this task; however, the traditional run-in requires additional patients, increasing times, and costs. The possible use of the available a-priori data could skip the run-in period. In this regard, ML (machine learning) techniques, which have recently shown considerable promising usage in clinical research, can be used to construct individual predictions of therapy response probability conditional on patient characteristics. An ensemble model of ML techniques was trained and validated on twin randomized clinical trials to mimic a run-in process within this framework. An ense

SUBMITTER: Lanera C 

PROVIDER: S-EPMC9482682 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature