Bayesian semiparametric regression for longitudinal binary processes with missing data.
Bayesian semiparametric regression for longitudinal binary processes with missing data.
复制标题
具有缺失数据的纵向二元过程的贝叶斯半参数回归。
DOI：
10.1002/sim.3265
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发表时间：
2008
影响因子：
2
通讯作者：
Hogan,JosephW
中科院分区：
文献类型：
--
作者：
Su,Li;Hogan,JosephW
Longitudinal studies with binary repeated measures are widespread in biomedical research. Marginal regression approaches for balanced binary data are well developed, whereas for binary process data, where measurement times are irregular and may differ by individuals, likelihood‐based methods for marginal regression analysis are less well developed. In this article, we develop a Bayesian regression model for analyzing longitudinal binary process data, with emphasis on dealing with missingness. We focus on the settings where data are missing at random (MAR), which require a correctly specified joint distribution for the repeated measures in order to draw valid likelihood‐based inference about the marginal mean. To provide maximum flexibility, the proposed model specifies both the marginal mean and serial dependence structures using nonparametric smooth functions. Serial dependence is allowed to depend on the time lag between adjacent outcomes as well as other relevant covariates. Inference is fully Bayesian. Using simulations, we show that adequate modeling of the serial dependence structure is necessary for valid inference of the marginal mean when the binary process data are MAR. Longitudinal viral load data from the HIV Epidemiology Research Study are analyzed for illustration. Copyright © 2008 John Wiley & Sons, Ltd.
