Problem
Research questions and friction points this paper is trying to address.
outcome delay
sample size re-estimation
clinical trial efficiency
overpowered trial
interim analysis
Evaluating the impact of outcome delay on the efficiency of sample size re-estimation
🤖 AI Summary
This study addresses the detrimental impact of delayed long-term outcomes in clinical trials on the efficiency of internal pilot sample size re-estimation, which can lead to over-enrollment and wasted resources. The authors systematically evaluate how outcome delay affects re-estimation designs and introduce two novel metrics—“delay impact” and “cost”—to jointly quantify its adverse effects on sample size estimation accuracy and statistical power. Using both continuous and binary endpoints, they analyze the distribution of re-estimated sample sizes under varying delay durations via root mean square error (RMSE) and the proposed metrics. Results demonstrate that longer delays substantially inflate average sample size and statistical power; notably, when the re-estimated sample size falls below the original plan, delay most severely exacerbates over-testing, posing the greatest risk to trial validity.
📝 Abstract
Sample size reestimation can be a powerful tool to ensure that a clinical trial meets its prespecified power requirements when uncertainty regarding a design parameter exists at the planning stage. However, long term primary endpoints can be harmful to the efficiency of this trial design. If recruitment is continued while treatment outcomes are awaited, long delay can potentially lead to a large number of pipeline participants being recruited in the trial that do not contribute to the interim analysis. This may lead to a larger number of recruited participants than are actually deemed required, resulting in an overpowered trial with high cost. This paper studies the exact impact of such outcome delay on the efficiency of internal pilot type SSR designs. The distribution of the final sample size post SSR is obtained under various delay lengths for both continuous and binary outcome data, how delay impacts the precision of the final sample size estimate is then discussed. Precisely, the impact of delay on this precision is assessed through RMSE, as well as two more novel metrics, termed the delay impact and cost. The results indicate that with increase in delay length, the delay impact increases, inflating average sample size and power. However, the severity of the effect of delayed outcomes depends highly on the exact trial setting. Trials where the reestimated sample size is smaller than originally planned suffer the most from delayed outcomes, often leading to an overpowered trial. However, the impact of delay is substantially less if the original planned sample size remains smaller than the reestimated sample size.
Innovation
Methods, ideas, or system contributions that make the work stand out.
sample size re-estimation
outcome delay
delay impact
internal pilot study
clinical trial efficiency
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