Dobson Angela, Henderson Robin
MRC Biostatistics Unit, Cambridge CB2 2SR, UK.
Biometrics. 2003 Dec;59(4):741-51. doi: 10.1111/j.0006-341x.2003.00087.x.
We present a variety of informal graphical procedures for diagnostic assessment of joint models for longitudinal and dropout time data. A random effects approach for Gaussian responses and proportional hazards dropout time is assumed. We consider preliminary assessment of dropout classification categories based on residuals following a standard longitudinal data analysis with no allowance for informative dropout. Residual properties conditional upon dropout information are discussed and case influence is considered. The proposed methods do not require computationally intensive methods over and above those used to fit the proposed model. A longitudinal trial into the treatment of schizophrenia is used to illustrate the suggestions.
我们提出了多种用于纵向和失访时间数据联合模型诊断评估的非正式图形程序。假设采用随机效应方法处理高斯响应和比例风险失访时间。我们基于标准纵向数据分析后的残差，在不考虑信息性失访的情况下，对失访分类类别进行初步评估。讨论了基于失访信息的残差特性，并考虑了个案影响。所提出的方法不需要除用于拟合所提出模型之外的计算密集型方法。一项关于精神分裂症治疗的纵向试验用于阐述这些建议。
