Comparison of novel methods in two-way enriched clinical trial design.


Journal

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

Informations de publication

Date de publication:
20 09 2019
Historique:
received: 14 03 2018
revised: 23 05 2019
accepted: 31 05 2019
pubmed: 1 7 2019
medline: 12 1 2021
entrez: 1 7 2019
Statut: ppublish

Résumé

Two-way enriched design (TED) is a novel approach addressing placebo response in clinical trials. It is a two-stage, randomized, placebo-controlled trial design with enrichment in placebo non-responders and treatment responders at the second stage. All data from the first stage and data from placebo non-responders and treatment responders in the second stage are used for the final analysis of the treatment effect. The existing methods for the analysis of TED data include score tests with one, two, and three degrees of freedom. All these methods are only applicable to binary outcomes. However, there is an interest in continuous outcomes in clinical trials in psychiatry. In this manuscript, we apply some novel methods, including a repeated measures model, a weighted repeated measures model with weights from propensity score, and weights from K-means clustering, to analyze TED data for both binary outcomes and continuous outcomes. The simulation study indicates that the repeated measures model performs consistently well in preserving the type I error and achieving the minimum mean standard error as well as a higher power. The performance of the weighted repeated measures model with weights from K-means clustering improves with increasing sample size. Investigators can choose from these analytic approaches under different scenarios.

Identifiants

pubmed: 31256435
doi: 10.1002/sim.8288
doi:

Substances chimiques

Placebos 0

Types de publication

Comparative Study Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

4112-4130

Informations de copyright

© 2019 John Wiley & Sons, Ltd.

Auteurs

Yuyin Liu (Y)

Department of Biostatistics, Boston University, Boston, Massachusetts.
Baim Institute for Clinical Research, Boston, Massachusetts.

Denis Rybin (D)

Pfizer Inc, Cambridge, Massachusetts.

Timothy C Heeren (TC)

Department of Biostatistics, Boston University, Boston, Massachusetts.

Gheorghe Doros (G)

Department of Biostatistics, Boston University, Boston, Massachusetts.
Baim Institute for Clinical Research, Boston, Massachusetts.

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Classifications MeSH