Increasing Power in Phase III Oncology Trials With Multivariable Regression: An Empirical Assessment of 535 Primary End Point Analyses.

IF 3.3 Q2 ONCOLOGY
Alexander D Sherry, Adina H Passy, Zachary R McCaw, Joseph Abi Jaoude, Timothy A Lin, Ramez Kouzy, Avital M Miller, Gabrielle S Kupferman, Esther J Beck, Pavlos Msaouel, Ethan B Ludmir
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Abstract

Purpose: A previous study demonstrated that power against the (unobserved) true effect for the primary end point (PEP) of most phase III oncology trials is low, suggesting an increased risk of false-negative findings in the field of late-phase oncology. Fitting models with prognostic covariates is a potential solution to improve power; however, the extent to which trials leverage this approach, and its impact on trial interpretation at scale, is unknown. To that end, we hypothesized that phase III trials using multivariable PEP analyses are more likely to demonstrate superiority versus trials with univariable analyses.

Methods: PEP analyses were reviewed from trials registered on ClinicalTrials.gov. Adjusted odds ratios (aORs) were calculated by logistic regressions.

Results: Of the 535 trials enrolling 454,824 patients, 69% (n = 368) used a multivariable PEP analysis. Trials with multivariable PEP analyses were more likely to demonstrate PEP superiority (57% [209 of 368] v 42% [70 of 167]; aOR, 1.78 [95% CI, 1.18 to 2.72]; P = .007). Among trials with a multivariable PEP model, 16 conditioned on covariates and 352 stratified by covariates. However, 108 (35%) of 312 trials with stratified analyses lost power by categorizing a continuous variable, which was especially common among immunotherapy trials (aOR, 2.45 [95% CI, 1.23 to 4.92]; P = .01).

Conclusion: Trials increasing power by fitting multivariable models were more likely to demonstrate PEP superiority than trials with unadjusted analysis. Underutilization of conditioning models and empirical power loss associated with covariate categorization required by stratification were identified as barriers to power gains. These findings underscore the opportunity to increase power in phase III trials with conventional methodology and improve patient access to effective novel therapies.

利用多变量回归提高 III 期肿瘤学试验的有效性:对 535 项主要终点分析的经验评估。
目的:之前的一项研究表明,大多数 III 期肿瘤学试验的主要终点(PEP)的(未观察到的)真实效应的功率很低,这表明在晚期肿瘤学领域出现假阴性结果的风险增加了。利用预后协变量拟合模型是一种提高功率的潜在解决方案;然而,试验利用这种方法的程度及其对大规模试验解释的影响尚不清楚。为此,我们假设,与采用单变量分析的试验相比,采用多变量PEP分析的III期试验更有可能显示出优越性:我们审查了在 ClinicalTrials.gov 上注册的试验的 PEP 分析。通过逻辑回归计算调整后的几率比(aORs):在有 454824 名患者参与的 535 项试验中,69%(n = 368)的试验采用了多变量 PEP 分析。采用多变量 PEP 分析的试验更有可能显示出 PEP 的优越性(57% [368 项试验中的 209 项] 对 42% [167 项试验中的 70 项];aOR,1.78 [95% CI,1.18 至 2.72];P = .007)。在采用多变量 PEP 模型的试验中,16 项试验以协变量为条件,352 项试验以协变量为分层条件。然而,在312项进行分层分析的试验中,有108项(35%)的试验因对连续变量进行分类而失去了作用力,这在免疫疗法试验中尤为常见(aOR,2.45 [95% CI,1.23~4.92];P = .01):结论:与采用未调整分析的试验相比,通过拟合多变量模型来提高功率的试验更有可能证明PEP的优越性。未充分利用调节模型和分层所需的协变量分类造成的经验功率损失被认为是提高功率的障碍。这些发现强调了在采用传统方法的III期试验中提高功率的机会,并改善了患者获得有效新型疗法的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
4.80%
发文量
190
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