A latent autoregressive model for longitudinal binary data subject to informative missingness |
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Authors: | Albert Paul S Follmann Dean A Wang Shaohua A Suh Edward B |
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Institution: | Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. albertp@ctep.nci.nih.gov |
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Abstract: | Longitudinal clinical trials often collect long sequences of binary data. Our application is a recent clinical trial in opiate addicts that examined the effect of a new treatment on repeated binary urine tests to assess opiate use over an extended follow-up. The dataset had two sources of missingness: dropout and intermittent missing observations. The primary endpoint of the study was comparing the marginal probability of a positive urine test over follow-up across treatment arms. We present a latent autoregressive model for longitudinal binary data subject to informative missingness. In this model, a Gaussian autoregressive process is shared between the binary response and missing-data processes, thereby inducing informative missingness. Our approach extends the work of others who have developed models that link the various processes through a shared random effect but do not allow for autocorrelation. We discuss parameter estimation using Monte Carlo EM and demonstrate through simulations that incorporating within-subject autocorrelation through a latent autoregressive process can be very important when longitudinal binary data is subject to informative missingness. We illustrate our new methodology using the opiate clinical trial data. |
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Keywords: | Informative missingness Longitudinal data Nonignorable missing data Repeated binary data |
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