Kleinman K P, Ibrahim J G, Laird N M
Department of Biostatistics, University of Michigan, Ann Arbor 48106, USA.
Biometrics. 1998 Mar;54(1):265-78.
In longitudinal clinical trials, one analysis of interest is an intention-to-treat analysis, which groups subjects according to the randomized treatment regardless of whether they stayed on that treatment or not. When in addition to going off the randomized treatment subjects may also drop out of the study and be lost to follow-up, it is unclear what an intention-to-treat analysis should be. If measurements are made after treatment drop-out on a random sample of subjects who drop the treatment, then Hogan and Laird (1996, Biometrics 52, 1002-1017) present a random effects model, well suited to this type of analysis, which fits a two-piece linear spline to the data with the knot at the time the assigned treatment is dropped. This article presents a Bayesian approach to fitting a similar two-piece linear spline model and shows how the model can be applied to data that have no off-treatment observations.
在纵向临床试验中，一种重要的分析方法是意向性分析，即不管受试者是否持续接受随机分配的治疗，都根据随机化治疗方案对他们进行分组。当除了停止接受随机分配的治疗外，受试者还可能退出研究且失访时，意向性分析应该如何进行尚不清楚。如果在受试者停止治疗后，对随机抽取的停止治疗的受试者样本进行测量，那么霍根和莱尔德（1996年，《生物统计学》第52卷，第1002 - 1017页）提出了一个随机效应模型，该模型非常适合这类分析，它对数据拟合了一个两段式线性样条，节点位于分配的治疗被停止之时。本文提出了一种贝叶斯方法来拟合类似的两段式线性样条模型，并展示了该模型如何应用于没有治疗后观测值的数据。
