[en] The amount of data collected from patients involved in clinical trials is
continuously growing. All baseline patient characteristics are potential covariates
that could be used to improve clinical trial analysis and power. However, the
limited number of patients in phases I and II studies restricts the possible number
of covariates included in the analyses. In this paper, we investigate the
cost/benefit ratio of including covariates in the analysis of clinical trials with a
continuous outcome. Within this context, we address the long-running question "What
is the optimum number of covariates to include in a clinical trial?" To further
improve the benefit/cost ratio of covariates, historical data can be leveraged to
pre-specify the covariate weights, which can be viewed as the definition of a new
composite covariate. Here we analyze the use of a composite covariate to improve the
estimated treatment effect in small clinical trials. A composite covariate limits
the loss of degrees of freedom and the risk of overfitting.
Disciplines :
Human health sciences: Multidisciplinary, general & others
Author, co-author :
Branders, Samuel; Tools4Patients
Pereira, Alvaro; Tools4Patients
Bernard, Guillaume; Tools4Patients
ERNST, Marie ; Centre Hospitalier Universitaire de Liège - CHU > Département de gestion des systèmes d'informations (GSI) > Secteur d'appui à la recherche clinique et biostatistique
Dananberg, Jamie; UNITY Biotechnology Inc., South San Francisco
Albert, Adelin ; Université de Liège - ULiège > Département des sciences de la santé publique > Département des sciences de la santé publique
Language :
English
Title :
Leveraging historical data to optimize the number of covariates and their explained variance in the analysis of randomized clinical trials.
Publication date :
2021
Journal title :
Statistical Methods in Medical Research
ISSN :
0962-2802
eISSN :
1477-0334
Publisher :
SAGE Publications, New York, United States - New York
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