A simulation study of approaches for handling disease progression in dose-finding clinical trials.

Dose-finding disease progression immunotherapy late-onset toxicity molecularly-targeted agent

Journal

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

Informations de publication

Date de publication:
03 2021
Historique:
pubmed: 16 9 2020
medline: 26 11 2021
entrez: 15 9 2020
Statut: ppublish

Résumé

In traditional dose-finding studies, dose-limiting toxicity (DLT) is determined within a fixed time observation window where DLT is often defined as a binary outcome. In the setting of oncology dose-finding trials, often patients in advanced stage of diseases are enrolled. Therefore, disease progression may occur within the DLT observation window leading to treatment discontinuation and rendering the patient unevaluable for DLT assessment. As a result, additional patients have to be enrolled, increasing the sample size. We propose and compare several practical approaches for handling disease progression which occurs within the DLT observation window, while in the framework of the time-to-event continual reassessment method (TITE-CRM) which allows using partial observations. The approaches differ on the way they define an evaluable patient and in the way incomplete observations are included. The practical approaches, which we call strategies A, B and C, are illustrated and contrasted in the context of a single simulated trial, and compared via simulations under various scenarios of dose-progression relationship, in the setting of advanced soft-tissue sarcoma.

Identifiants

pubmed: 32931360
doi: 10.1080/10543406.2020.1814796
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

156-167

Auteurs

Lucie Biard (L)

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
INSERM U1153 team ECSTRRA, Université de Paris, Paris, France.

Bin Cheng (B)

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.

Gulam A Manji (GA)

Division of Medical Oncology, Columbia University Irving Medical Center, and New York Presbyterian Hospital, Herbert Irving Pavilion, New York, NY, USA.

Shing M Lee (SM)

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.

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Classifications MeSH