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Activity Number:
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340
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Type:
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Invited
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Date/Time:
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Wednesday, August 14, 2002 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section*
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| Abstract - #300040 |
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Title:
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A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness
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Author(s):
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Paul Albert*+ and Dean Follman
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Affiliation(s):
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National Cancer Institute and National Institute of Health
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Address:
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Executive Plaza North, Room 8136, Bethesda , Maryland, 20892, USA
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Keywords:
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missing data ; shared parameter ; random effects ; latent process
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Abstract:
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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. 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 are subject to informative missingness. We illustrate our new methodology using opiate clinical trial data.
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