Estimation of treatment effect for the sequential parallel design.

TitleEstimation of treatment effect for the sequential parallel design.
Publication TypeJournal Article
Year of Publication2011
AuthorsTamura, Roy N., Xiaohong Huang, and Dennis D. Boos
JournalStat Med
Volume30
Issue30
Pagination3496-506
Date Published2011 Dec 30
ISSN1097-0258
KeywordsBias, Biostatistics, Depressive Disorder, Major, Humans, Likelihood Functions, Linear Models, Mental Disorders, Randomized Controlled Trials as Topic, Serotonin Uptake Inhibitors, Tetrahydrofolates, Treatment Outcome
Abstract

The sequential parallel clinical trial is a novel clinical trial design being used in psychiatric diseases that are known to have potentially high placebo response rates. The design consists of an initial parallel trial of placebo versus drug augmented by a second parallel trial of placebo versus drug in the placebo non-responders from the initial trial. Statistical research on the design has focused on hypothesis tests. However, an equally important output from any clinical trial is the estimate of treatment effect and variability around that estimate. In the sequential parallel trial, the most important treatment effect is the effect in the overall population. This effect can be estimated by considering only the first phase of the trial, but this ignores useful information from the second phase of the trial. We develop estimates of treatment effect that incorporate data from both phases of the trial. Our simulations and a real data example suggest that there can be substantial gains in precision by incorporating data from both phases. The potential gains appear to be greatest in moderate-sized trials, which would typically be the case in phase II trials.

DOI10.1002/sim.4412
Alternate JournalStat Med
Original PublicationEstimation of treatment effect for the sequential parallel design.
PubMed ID22139795
PubMed Central IDPMC3882761
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P01 CA142538-01 / CA / NCI NIH HHS / United States
Project: