Design and monitoring of survival trials in complex scenarios.

delayed treatment effect intent-to-treat non-uniform accrual piecewise exponential survival trials treatment crossover treatment dilution

Journal

Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016

Informations de publication

Date de publication:
30 01 2019
Historique:
received: 11 12 2017
revised: 03 07 2018
accepted: 28 08 2018
pubmed: 4 10 2018
medline: 7 3 2020
entrez: 4 10 2018
Statut: ppublish

Résumé

This paper proposes an approach to design and monitor survival trials accounting for complex scenarios such as delayed treatment effect, treatment dilution, and treatment crossover. These scenarios often lead to non-proportional hazards, making study design and monitoring more difficult. We demonstrate that, with event times following piecewise exponential distributions, the log-rank statistic as well as its variance-covariance structure can be easily computed, which greatly simplifies study design and monitoring. As the number of pieces in the exponential distributions can be arbitrary, this approach can handle a wide range of scenarios. Three hypothetical examples are used to demonstrate its potential use.

Identifiants

pubmed: 30281165
doi: 10.1002/sim.7975
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

192-209

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2018 John Wiley & Sons, Ltd.

Auteurs

Xiaodong Luo (X)

Department of Biostatistics and Programming, Research and Development, Sanofi US, Bridgewater, New Jersey.

Xuezhou Mao (X)

Department of Biostatistics and Programming, Research and Development, Sanofi US, Bridgewater, New Jersey.

Xun Chen (X)

Department of Biostatistics and Programming, Research and Development, Sanofi US, Bridgewater, New Jersey.

Junshan Qiu (J)

DB1, Office of Biostatistics, Office of Translational Science, CDER, FDA, Silver Spring, Maryland.

Steven Bai (S)

DB1, Office of Biostatistics, Office of Translational Science, CDER, FDA, Silver Spring, Maryland.

Hui Quan (H)

Department of Biostatistics and Programming, Research and Development, Sanofi US, Bridgewater, New Jersey.

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