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首页> 外文期刊>Journal of applied statistics >How Cox models react to a study-specific confounder in a patient-level pooled dataset: random effects better cope with an imbalanced covariate across trials unless baseline hazards differ
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How Cox models react to a study-specific confounder in a patient-level pooled dataset: random effects better cope with an imbalanced covariate across trials unless baseline hazards differ

机译：Cox模型如何在患者水平的汇总数据集中对研究特定混杂因素做出反应：除非基线风险不同，随机效应更好地应对整个试验中的协变量不平衡

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摘要

Combining patient-level data from clinical trials can connect rare phenomena with clinical endpoints, but statistical techniques applied to a single trial may become problematical when trials are pooled. Estimating the hazard of a binary variable unevenly distributed across trials showcases a common pooled database issue. We studied how an unevenly distributed binary variable can compromise the integrity of fixed and random effects Cox proportional hazards (cph) models. We compared fixed effect and random effects cph models on a set of simulated datasets inspired by a 17-trial pooled database of patients presenting with ST segment elevation myocardial infarction (STEMI) and non-STEMI undergoing percutaneous coronary intervention. An unevenly distributed covariate can bias hazard ratio estimates, inflate standard errors, raise type I error, and reduce power. While uneveness causes problems for all cph models, random effects suffer least. Compared to fixed effect models, random effects suffer lower bias and trade inflated type I errors for improved power. Contrasting hazard rates between trials prevent accurate estimates from both fixed and random effects models.
机译：将来自临床试验的患者水平数据组合起来可以将罕见现象与临床终点联系起来，但是合并试验时，应用于单个试验的统计技术可能会出现问题。估算在试验中分布不均的二元变量的危害显示了一个常见的汇总数据库问题。我们研究了分布不均的二进制变量如何损害固定效应和随机效应Cox比例风险（cph）模型的完整性。我们在一组模拟数据集上比较了固定效应和随机效应cph模型，这些数据集受到17例合并ST段抬高型心肌梗塞（STEMI）和非STEMI接受经皮冠状动脉介入治疗的患者的启发。分布不均的协变量可能会使危险比估计值产生偏差，膨胀标准误差，增加I型误差并降低功效。尽管不均匀性会导致所有cph模型出现问题，但随机效应的影响最小。与固定效应模型相比，随机效应具有较低的偏差，并且可以增加虚假的I型误差以提高功率。两次试验之间的危险率不同，无法从固定效应模型和随机效应模型中得出准确的估计值。

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