Liao Lauren D, Højbjerre-Frandsen Emilie, Hubbard Alan E, Schuler Alejandro
Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Biostatistics Methods and Outreach, Novo Nordisk A/S, Bagsvaerd, Denmark.
Int J Biostat. 2025 Mar 11;21(1):1-15. doi: 10.1515/ijb-2024-0018. eCollection 2025 May 1.
Although randomized controlled trials (RCTs) are a cornerstone of comparative effectiveness, they typically have much smaller sample size than observational studies due to financial and ethical considerations. Therefore there is interest in using plentiful historical data (either observational data or prior trials) to reduce trial sizes. Previous estimators developed for this purpose rely on unrealistic assumptions, without which the added data can bias the treatment effect estimate. Recent work proposed an alternative method (prognostic covariate adjustment) that imposes no additional assumptions and increases efficiency in trial analyses. The idea is to use historical data to learn a prognostic model: a regression of the outcome onto the covariates. The predictions from this model, generated from the RCT subjects' baseline variables, are then used as a covariate in a linear regression analysis of the trial data. In this work, we extend prognostic adjustment to trial analyses with nonparametric efficient estimators, which are more powerful than linear regression. We provide theory that explains why prognostic adjustment improves small-sample point estimation and inference without any possibility of bias. Simulations corroborate the theory: efficient estimators using prognostic adjustment compared to without provides greater power (i.e., smaller standard errors) when the trial is small. Population shifts between historical and trial data attenuate benefits but do not introduce bias. We showcase our estimator using clinical trial data provided by Novo Nordisk A/S that evaluates insulin therapy for individuals with type 2 diabetes.
尽管随机对照试验（RCT）是比较疗效研究的基石，但由于财务和伦理方面的考虑，其样本量通常比观察性研究小得多。因此，人们有兴趣利用丰富的历史数据（观察性数据或先前的试验数据）来减少试验规模。此前为此目的开发的估计方法依赖于不切实际的假设，没有这些假设，额外的数据可能会使治疗效果估计产生偏差。最近的研究提出了一种替代方法（预后协变量调整），该方法无需额外假设，并提高了试验分析的效率。其思路是利用历史数据来学习一个预后模型：将结局回归到协变量上。然后，根据随机对照试验受试者的基线变量生成的该模型预测值，将其用作试验数据线性回归分析中的一个协变量。在这项研究中，我们将预后调整扩展到使用非参数有效估计量的试验分析中，非参数有效估计量比线性回归更强大。我们提供了理论来解释为什么预后调整能改善小样本点估计和推断，且不存在任何偏差的可能性。模拟结果证实了该理论：当试验规模较小时，与未使用预后调整相比，使用预后调整的有效估计量具有更大的功效（即更小的标准误差）。历史数据和试验数据之间的总体变化会减弱益处，但不会引入偏差。我们使用诺和诺德公司提供的评估2型糖尿病患者胰岛素治疗的临床试验数据展示了我们的估计量。
