Powering Bias and Clinically Important Treatment Effects in Randomized Trials of Critical Illness

Abstract

Objectives: Recurring issues in clinical trial design may bias results toward the null, yielding findings inconclusive for treatment effects. This study evaluated for powering bias among high-impact critical care trials and the associated risk of masking clinically important treatment effects. Design, Setting, and Patients: Secondary analysis of multicenter randomized trials of critically ill adults in which mortality was the main endpoint. Trials were eligible for inclusion if published between 2008 and 2018 in leading journals. Analyses evaluated for accuracy of estimated control group mortality, adaptive sample size strategy, plausibility of predicted treatment effect, and results relative to the minimal clinically important difference. The main outcome was the mortality risk difference at the study-specific follow-up interval. Interventions: None. Measurements and Main Results: Of 101 included trials, 12 met statistical significance for their main endpoint, five for increased intervention-associated mortality. Most trials (77.3%) overestimated control group mortality in power calculations (observed minus predicted difference,-6.7% ± 9.8%; p < 0.01). Due to this misestimation of control group mortality, in 14 trials, the intervention would have had to prevent at least half of all deaths to achieve the hypothesized treatment effect. Seven trials prespecified adaptive sample size strategies that might have mitigated this issue. The observed risk difference for mortality fell within 5% of predicted in 20 trials, of which 16 did not reach statistical significance. Half of trials (47.0%) were powered for an absolute risk reduction greater than or equal to 10%, but this effect size was observed in only three trials with a statistically significant treatment benefit. Most trials (67.3%) could not exclude clinically important treatment benefit or harm. Conclusions: The design of most high-impact critical care trials biased results toward the null by overestimating control group mortality and powering for unrealistic treatment effects. Clinically important treatment effects often cannot be excluded.