Utility of propensity score-based Bayesian borrowing of external adult data in pediatric trials: A pragmatic evaluation through a case study in acute lymphoblastic leukemia (ALL).


Journal

Journal of biopharmaceutical statistics
ISSN: 1520-5711
Titre abrégé: J Biopharm Stat
Pays: England
ID NLM: 9200436

Informations de publication

Date de publication:
02 11 2023
Historique:
medline: 2 11 2023
pubmed: 6 1 2023
entrez: 5 1 2023
Statut: ppublish

Résumé

A fully powered randomized controlled cancer trial can be challenging to conduct in children because of difficulties in enrollment of pediatric patients due to low disease incidence. One way to improve the feasibility of trials in pediatric patients, when clinically appropriate, is through borrowing information from comparable external adult trials in the same disease. Bayesian analysis of a pediatric trial provides a way of seamlessly augmenting pediatric trial efficacy data with data from external adult trials. However, not all external adult trial subjects may be equally clinically relevant with respect to the baseline disease severity, prognostic factors, co-morbidities, and prior therapy observed in the pediatric trial of interest. The propensity score matching method provides a way of matching the external adult subjects to the pediatric trial subjects on a set of clinically determined baseline covariates, such as baseline disease severity, prognostic factors and prior therapy. The matching then allows Bayesian information borrowing from only the most clinically relevant external adult subjects. Through a case study in pediatric acute lymphoblastic leukemia (ALL), we examine the utility of propensity score matched mixture and power priors in bringing appropriate external adult efficacy information into pediatric trial efficacy assessment, and present considerations for scaling fixed borrowing from external adult data.

Identifiants

pubmed: 36600441
doi: 10.1080/10543406.2022.2162069
doi:

Types de publication

Randomized Controlled Trial Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

737-751

Auteurs

Antara Majumdar (A)

Oncology Biostatistics, GlaxoSmithKline, Collegeville, PA, USA.

Rebecca Rothwell (R)

Office of Biostatistics, U.S. Food and Drug Administration, Silver Spring, MD, USA.

Gregory Reaman (G)

Oncology Center of Excellence, U.S. Food and Drug Administration, Silver Spring, MD, USA.

Corinne Ahlberg (C)

Acorn AI by Medidata, a Dassault Systèmes company, New York, NY, USA.

Pourab Roy (P)

Biostatistics, Regeneron Pharmaceuticals, Tarrytown, NY, USA.

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Classifications MeSH