Key result
Clinical trials stopped early for benefit tend to overestimate treatment effects and introduce conditional bias, though their exclusion from meta-analyses is not recommended as it introduces underestimation bias.
Why the study?
Although widely used, sequential analysis in clinical trials carries issues such as balancing safety and efficacy, overestimating intervention effect sizes, and conditional bias.
This review highlights the methodological challenges of early-stopped clinical trials, including effect size overestimation and conditional bias, which can affect the interpretation of meta-analyses.
Sequential analysis risks overestimating effect sizes and safety-efficacy imbalances; leaves open optimal stopping rules for trials.
Sequential analysis of clinical trials allows researchers a continuous monitoring of emerging data and greater security to avoid subjecting the trial participants to a less effective therapy before the inferiority is evident, while controlling the overall error rate. Although it has been widely used since its development, sequential analysis is not problem-free. Among them main issues to be mentioned are the balance between safety and efficacy, overestimation of the effect size of interventions and conditional bias. In this review, we develop different aspects of this methodology and the impact of including early-stopped clinical trials in systematic reviews with meta-analysis.
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Garegnani et al. (2020) reported a review. Sequential analysis and early stopping of clinical trials was evaluated. Clinical trials stopped early for benefit tend to overestimate treatment effects and introduce conditional bias, though their exclusion from meta-analyses is not recommended as it introduces underestimation bias.
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