Biometrics Unit, Cancer Institute of Montpellier, University of Montpellier, Montpellier, France.
French National Platform Quality of Life and Cancer, Montpellier, France.
BMC Med Res Methodol. 2023 Feb 10;23(1):36. doi: 10.1186/s12874-023-01846-3.
Patient-reported outcomes such as health-related quality of life (HRQoL) are increasingly used as endpoints in randomized cancer clinical trials. However, the patients often drop out so that observation of the HRQoL longitudinal outcome ends prematurely, leading to monotone missing data. The patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. In case of informative dropout, the usual linear mixed model analysis will produce biased estimates. Unbiased estimates cannot be obtained unless the dropout is jointly modeled with the longitudinal outcome, for instance by using a joint model composed of a linear mixed (sub)model linked to a survival (sub)model. Our objective was to investigate in a clinical trial context the consequences of using the most frequently used linear mixed model, the random intercept and slope model, rather than its corresponding joint model.
We first illustrate and compare the models on data of patients with metastatic pancreatic cancer. We then perform a more formal comparison through a simulation study.
From the application, we derived hypotheses on the situations in which biases arise and on their nature. Through the simulation study, we confirmed and complemented these hypotheses and provided general explanations of the bias mechanisms.
In particular, this article reveals how the linear mixed model fails in the typical situation where poor HRQoL is associated with an increased risk of dropout and the experimental treatment improves survival. Unlike the joint model, in this situation the linear mixed model will overestimate the HRQoL in both arms, but not equally, misestimating the difference between the HRQoL trajectories of the two arms to the disadvantage of the experimental arm.
患者报告的结果（如健康相关生活质量（HRQoL））越来越多地被用作癌症随机临床试验的终点。然而，患者经常中途退出，导致 HRQoL 纵向结果的观察提前结束，导致单调缺失数据。患者可能因各种原因退出，包括出现毒性、疾病进展或死亡。在信息性退出的情况下，通常的线性混合模型分析会产生有偏差的估计。除非将退出与纵向结果联合建模，例如使用由线性混合（子）模型链接到生存（子）模型组成的联合模型，否则无法获得无偏估计。我们的目的是在临床试验背景下研究使用最常用的线性混合模型（随机截距和斜率模型）而不是其相应的联合模型的后果。
我们首先在转移性胰腺癌患者的数据上说明并比较了这些模型。然后，我们通过模拟研究进行了更正式的比较。
从应用中，我们得出了关于出现偏差的情况及其性质的假设。通过模拟研究，我们证实并补充了这些假设，并提供了偏差机制的一般解释。
特别是，本文揭示了线性混合模型在 HRQoL 较差与退出风险增加以及实验治疗改善生存相关的典型情况下如何失败。与联合模型不同，在这种情况下，线性混合模型将高估两个臂中的 HRQoL，但并不均等，错误估计两个臂的 HRQoL 轨迹之间的差异，对实验臂不利。
