Abstract
We develop a novel modeling strategy for analyzing data with repeated binary responses over time as well as time-dependent missing covariates. We assume that covariates are missing at random (MAR). We use the generalized linear mixed logistic regression model for the repeated binary responses and then propose a joint model for time-dependent missing covariates using information from different sources. A Monte Carlo EM algorithm is developed for computing the maximum likelihood estimates. We propose an extended version of the AIC criterion to identify the important factors that m a y explain the binary responses. A real plant dataset is used to motivate and illustrate the proposed methodology.
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Huang, L., Chen, MH., Yu, F. et al. On modeling repeated binary responses and time-dependent missing covariates. JABES 13, 270–293 (2008). https://doi.org/10.1198/108571108X338023
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DOI: https://doi.org/10.1198/108571108X338023