A mixed effects Markov model for repeated binary outcomes with non-ignorable dropout.
A mixed effects Markov model for repeated binary outcomes with non-ignorable dropout.
复制标题
具有不可忽略的丢失的重复二元结果的混合效应马尔可夫模型。
DOI：
10.1002/sim.2333
复制
发表时间：
2006
影响因子：
2
通讯作者：
Crits-Christoph,Paul
中科院分区：
文献类型：
--
作者：
Gallop,RobertJ;TenHave,ThomasR;Crits-Christoph,Paul
在许多研究领域中，重复的二进制测量通常代表两个状态的随机过程，其中个体可以在两个状态之间转换。在行为或身体残疾的情况下，个人可以从易感或阈下状态转变为感染或症状状态，然后回到阈下状态。状态之间的转换经常发生在连续的时间内，但在离散的，不规则间隔的时间点上观察到，这可能是每个人所独有的。这些数据的分析方法通常基于马尔可夫假设。Cook（生物统计学1999; 55：915-920）引入了一个条件马尔可夫模型，该模型适应了模型参数中具有随机效应的受试者间变化。我们通过在模型中添加一个不可分解的dropout组件来扩展此模型。随机效应的分布的规范，以保证一个封闭的形式表达的边际似然。这种方法是说明应用程序的数据集从寄生虫感染现场调查，可卡因治疗研究的数据集，和一个老化研究的数据集。模拟表明，共享参数模型相对于至少一个替代的不可分解模型是稳健的。版权所有© 2005年约翰威利父子有限公司。
In many areas of research, repeated binary measures often represent a two‐state stochastic process, where individuals can transition among two states. In a behavioural or physical disability setting, individuals can flow from susceptible or subthreshold state, to an infectious or symptomatic state, and back to a subthreshold state. Quite often the transition among the states happens in continuous time but is observed at discrete, irregularly spaced timepoints which may be unique to each individual. Methods for analyses of such data are typically based on the Markov assumption. Cook (Biometrics1999;55:915–920) introduced a conditional Markov model that accommodates the subject‐to‐subject variation in the model parameters with random effects. We extend this model by adding a non‐ignorable dropout component to the model. Specification of the distribution of the random effects is made to guarantee a closed form expression of the marginal likelihood. This methodology is illustrated by applications to a data set from a parasitic field infection survey, a data set from a cocaine treatment study, and a data set from an aging study. Simulations suggest that the shared parameter model is robust with respect to at least one alternative non‐ignorable model. Copyright © 2005 John Wiley & Sons, Ltd.