病人报告的结果，例如与健康相关的生命质量（HRQoL），越来越多地被用作随机癌症临床试验的终点。然而，由于病人常常退出，导致对HRQoL长期结果的观察过早结束，进而出现单调缺失数据。病人可能因为各种原因退出，包括毒性反应的发生、疾病进展或死亡。在有信息的退出情况下，通常的线性混合模型分析会产生偏差估计。除非将退出与长期结果联合建模（例如使用由线性混合（子）模型连接到生存（子）模型组成的联合模型），否则无法得到无偏估计。我们的目标是在临床试验环境中调查使用最常用的线性混合模型——随机拦截和斜率模型而不是其对应的联合模型所产生的后果。我们首先通过转移性胰腺癌患者的数据说明并比较这些模型。然后我们通过模拟研究进行更正式的比较。从应用中，我们推导出在哪些情况下会出现偏差以及其性质的假设。通过模拟研究，我们证实并补充了这些假设，并提供了关于偏差机制的普遍解释。特别是，本文揭示了线性混合模型在典型情况下失败的情形，即HRQoL不佳与退出风险增加，实验治疗则提高生存机会。与联合模型不同，这种情况下线性混合模型将高估两个分组的HRQoL，但估计误差不同，会对实验组HRQoL轨迹之间的差异带来不利影响。© 2023。作者。

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.© 2023. The Author(s).