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Sequential likelihood ratios and e-processes in the analysis of the RENOVATE trial
Fernando G Zampieri1,2, Alexandre B Cavalcanti1, Peter M B Cahusac3
1Research Institute, Hospital do Coracao (HCor), Sao Paulo, Brazil.
Annals of the American Thoracic Society
|February 24, 2026
Summary
Adaptive platform trials can yield misleading results due to changing patient populations. Evidential methods reveal true treatment effects and enrollment dynamics, offering a diagnostic tool for complex trial designs.
Area of Science:
- Clinical Trials
- Biostatistics
- Evidence Synthesis
Background:
- Adaptive platform trials face challenges with heterogeneous populations and non-concurrent enrollment.
- Pooled analyses in such trials can be misleading due to shifting patient composition over time.
Purpose of the Study:
- To quantify treatment effect heterogeneity in the RENOVATE trial using evidential methods and e-processes.
- To demonstrate how temporal shifts in enrollment composition create statistical artifacts in sequential monitoring.
Main Methods:
- Secondary analysis of the RENOVATE trial (n=1,766) comparing high-flow nasal oxygen (HFNO) vs. noninvasive ventilation (NIV) across five populations.
- Computed sequential likelihood ratio (SLR) processes within groups for death or intubation at 7 days.
- Sensitivity analyses used conditional and randomization-based e-processes.
Main Results:
- Substantial treatment effect heterogeneity observed across populations; cardiogenic edema showed HFNO benefit, while COVID-19 suggested potential harm.
- Pooled SLR analysis exhibited a V-shaped artifact due to enrollment composition shifts, particularly during the COVID-19 period.
- Alternative analyses aligned with SLR but avoided artifact interpretation.
Conclusions:
- Sequential evidential analysis effectively reveals treatment heterogeneity masked by pooled analyses in adaptive platform trials.
- Enrollment composition mechanistically drives evidence accumulation trajectories.
- E-processes serve as a diagnostic tool for platform trials, visualizing interactions between enrollment dynamics and treatment heterogeneity.
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