Ann Arbor Pharmacometrics Group (A2PG), 110 E Miller, Garden Suite, Ann Arbor, MI 48104, USA.
Stat Med. 2011 Apr 30;30(9):935-49. doi: 10.1002/sim.4155. Epub 2011 Jan 13.
Continuous bounded outcome data are unlikely to meet the usual assumptions for mixed-effects models of normally distributed and independent subject-specific and residual random effects. Additionally, overly complicated model structures might be necessary to account adequately for non-drug (time-dependent) and drug treatment effects. A transformation strategy with a likelihood component for censoring is developed to promote the simplicity of model structures and to improve the plausibility of assumptions on the random effects. The approach is motivated by Health Assessment Questionnaire Disability Index (HAQ-DI) data from a study in subjects with rheumatoid arthritis and is evaluated using a simulation study.
连续有界的结果数据不太可能满足通常的假设，即混合效应模型适用于正态分布和独立的个体特定随机效应和残差随机效应。此外，可能需要过于复杂的模型结构来充分说明非药物（时间相关）和药物治疗效果。开发了一种带有删失似然成分的转换策略，以促进模型结构的简单性，并提高对随机效应的假设的合理性。该方法的动机是来自类风湿关节炎患者研究的健康评估问卷残疾指数 (HAQ-DI) 数据，并使用模拟研究进行评估。
