Reverse Selection Designs for Accommodating Multiple Control Arms

Evaluating a novel treatment in a randomized controlled trial requires comparison against existing therapies. If several existing therapies of similar benefit exist, the identification of a single control regimen may be difficult. For this situation, we propose a reverse selection design which, in its simplest form, includes a single experimental treatment arm and two control arms. Rather than carrying both control arms through the entire trial, the control arms are compared at an early interim analysis, ideally while accrual is ongoing. At this time, the worst-performing control arm is dropped and randomization continues to the remaining arms. At the end of the study, we compare the treatment to the remaining control arm. When no head-to-head comparison of the extant therapies is available or feasible, this design requires a smaller sample size than a traditional three-arm design or two sequential trials in which the extant therapies are compared and the better treatment is used in a subsequent trial as the control arm. This is because the final comparison is only between two arms and because the early interim analysis occurs prior to the end of accrual-yet with enough information such that any substantially better control arm will be selected. We evaluate the operating characteristics of a reverse selection design via simulation and show that it reduces the required sample size needed to compare the treatment against the best control, controls type I error, and likely selects the right control arm to use in the final analysis.

Authors

Institutions

Publication Details

Journal
Clinical Cancer Research
Published
2024-10-18
DOI
https://doi.org/10.1158/1078-0432.ccr-24-1282
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reverse Selection Designs for Accommodating Multiple Control Arms

Michael C. Wu, Sarah Samorodnitsky, Megan Othus, Michael LeBlanc
Clinical Cancer Research
Statistical Methods in Clinical Trials
article

Reverse Selection Designs for Accommodating Multiple Control Arms

Michael C. Wu, Sarah Samorodnitsky, Megan Othus, Michael LeBlanc
article en

Abstract

Evaluating a novel treatment in a randomized controlled trial requires comparison against existing therapies. If several existing therapies of similar benefit exist, the identification of a single control regimen may be difficult. For this situation, we propose a reverse selection design which, in its simplest form, includes a single experimental treatment arm and two control arms. Rather than carrying both control arms through the entire trial, the control arms are compared at an early interim analysis, ideally while accrual is ongoing. At this time, the worst-performing control arm is dropped and randomization continues to the remaining arms. At the end of the study, we compare the treatment to the remaining control arm. When no head-to-head comparison of the extant therapies is available or feasible, this design requires a smaller sample size than a traditional three-arm design or two sequential trials in which the extant therapies are compared and the better treatment is used in a subsequent trial as the control arm. This is because the final comparison is only between two arms and because the early interim analysis occurs prior to the end of accrual-yet with enough information such that any substantially better control arm will be selected. We evaluate the operating characteristics of a reverse selection design via simulation and show that it reduces the required sample size needed to compare the treatment against the best control, controls type I error, and likely selects the right control arm to use in the final analysis.

Clinical Cancer ResearchVol. 30(24)
Fred Hutch Cancer Center (US)
Hope Foundation, National Institutes of Health
Good health and well-being
Openalex Percentile: Top 81%
Statistical Methods in Clinical Trials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Reverse Selection Designs for Accommodating Multiple Control Arms — Michael C. Wu, Sarah Samorodnitsky, et al. · Clinical Cancer Research (2024) | TGRS Research Map | TGRS