A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness
Article first published online: 21 MAY 2004
Volume 58, Issue 3, pages 631–642, September 2002
How to Cite
Albert, P. S., Follmann, D. A., Wang, S. A. and Suh, E. B. (2002), A Latent Autoregressive Model for Longitudinal Binary Data Subject to Informative Missingness. Biometrics, 58: 631–642. doi: 10.1111/j.0006-341X.2002.00631.x
- Issue published online: 21 MAY 2004
- Article first published online: 21 MAY 2004
- Received February 2001. Revised March 2002. Accepted April 2002.
- Informative missingness;
- Longitudinal data;
- Nonignorable missing data;
- Repeated binary data
Summary. 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.