基于事件驱动的规划,使用二元终点进行双臂试验.
Erica H Brittain1, Raphaël N Morsomme1, Michael A Proschan1
1Office of Biostatistics Research, NIAID, NIH, Bethesda, MD, USA.
Clinical trials (London, England)
|February 16, 2026
概括
对于具有较低事件概率的临床试验,事件数比样本大小更稳定. 这种稳定性可以增强适应性试验设计,并为二进制终点提供简单的事件驱动策略.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 统计力量分析 统计力量分析
背景情况:
- 用二进制终点进行临床试验的样本大小计算通常依赖于事件概率,这可能是不确定的.
- 生存试验是由事件数驱动的,比样本大小对未知参数的敏感性要小.
- 这项研究调查了事件计数与样本大小在双臂随机试验中的相对稳定性.
研究的目的:
- 量化二元终点试验中事件数与样本大小相比的相对稳定性.
- 探索使用这种相对稳定的适应性试验设计的增强.
- 在这样的环境中,评估简单的事件驱动策略的潜在好处.
主要方法:
- 使用样本大小公式来评估事件数的稳定性与相对风险,赔率比率和风险差异的样本大小.
- 在相对稳定的条件下进行模拟以评估事件驱动设计.
- 使用各种分析方法和试验停止策略评估I型错误率和功率.
主要成果:
- 事件的数量是相对风险 (事件概率 < 1/3) 和赔率比率 (事件概率 < 0.20) 的至少三倍比样本大小更稳定.
- 这种稳定性独立于错误率和治疗效应的大小.
- 事件驱动设计的模拟表明,虽然非对称方法可能会增加I型错误,但其他方法表现出有利的操作特征.
结论:
- 在具有中度低事件概率的试验中,专注于事件数量可以帮助试验规划和徒劳性评估.
- 这种方法可以促进对二进制终点的简单,可行和有吸引力的事件驱动设计的开发.
- 采用以事件为中心的视角可以简化临床试验的规划和执行.
相关概念视频
01:16Crossover Experiments
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
01:15Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:23McNemar's Test
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...