Type I error control and interim monitoring for co-primary hypotheses involving a subgroup in the Outpatient Treatment with Anti-Coronavirus Immunoglobulin (OTAC) trial

The recent growth of immunoglobulin-based therapies has motivated clinical trials testing primary endpoints both in the overall cohort and in subgroups of patients, such as in patients without specific antibodies at baseline. Multiple testing methods in clinical trials often ignore the natural correlation between test statistics in such contexts, resulting in overly conservative type I error control. The Outpatient Treatment with Anti-Coronavirus Immunoglobulin (OTAC) trial, is an ongoing Phase III trial evaluating the effect of a single infusion of anti-COVID-19 hyperimmune intravenous immunoglobulin (hIVIG), in outpatient adults with recently diagnosed SARS-CoV-2 infection, in both the overall cohort and in the subgroup of participants who had not received monoclonal antibodies or antiviral treatments. We present the method used to control the type I error at a predetermined rate while taking the estimated correlation into account, thus increasing efficiency. We evaluated the operating characteristics of this method in both fixed and group-sequential scenarios through extensive simulation studies. Our findings indicate that this approach controls the type I error at the desired rate, improves power, and reduces the expected sample size compared to a Bonferroni correction. <b>Trial Registration</b>: This study was registered on clinicaltrials.gov under NCT0491026 on 1 June 2021.

Authors

Institutions

Publication Details

Journal
Contemporary Clinical Trials Communications
Published
2025-12-24
DOI
https://doi.org/10.1016/j.conctc.2025.101592
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Type I error control and interim monitoring for co-primary hypotheses involving a subgroup in the Outpatient Treatment with Anti-Coronavirus Immunoglobulin (OTAC) trial

Lianne Siegel, Cavan Reilly, Abdel G. Babiker, Jason V. Baker et al.
Contemporary Clinical Trials Communications
Statistical Methods in Clinical Trials
article

Type I error control and interim monitoring for co-primary hypotheses involving a subgroup in the Outpatient Treatment with Anti-Coronavirus Immunoglobulin (OTAC) trial

Lianne Siegel, Cavan Reilly, Abdel G. Babiker, Jason V. Baker, Jiayi Hu
article en

Abstract

The recent growth of immunoglobulin-based therapies has motivated clinical trials testing primary endpoints both in the overall cohort and in subgroups of patients, such as in patients without specific antibodies at baseline. Multiple testing methods in clinical trials often ignore the natural correlation between test statistics in such contexts, resulting in overly conservative type I error control. The Outpatient Treatment with Anti-Coronavirus Immunoglobulin (OTAC) trial, is an ongoing Phase III trial evaluating the effect of a single infusion of anti-COVID-19 hyperimmune intravenous immunoglobulin (hIVIG), in outpatient adults with recently diagnosed SARS-CoV-2 infection, in both the overall cohort and in the subgroup of participants who had not received monoclonal antibodies or antiviral treatments. We present the method used to control the type I error at a predetermined rate while taking the estimated correlation into account, thus increasing efficiency. We evaluated the operating characteristics of this method in both fixed and group-sequential scenarios through extensive simulation studies. Our findings indicate that this approach controls the type I error at the desired rate, improves power, and reduces the expected sample size compared to a Bonferroni correction. <b>Trial Registration</b>: This study was registered on clinicaltrials.gov under NCT0491026 on 1 June 2021.

Contemporary Clinical Trials CommunicationsVol. 49
University of Minnesota (US), MRC Clinical Trials Unit at UCL (GB), University of Minnesota Medical Center (US), Minnesota Department of Health (US), Hennepin Healthcare Research Institute (US), University College London (GB), Medical Research Council (GB)
Good health and well-being
Openalex Percentile: Top 24%
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.