A likelihood ratio test for nonignorable missingness in incomplete binary longitudinal data

Missing data are common in many clinical studies. When missingness is non-
ignorable, a full likelihood analysis of the data requires incorporating a missing
data model into the observed data likelihood function. In this article, we study
the bias of the ML estimator when the corresponding maximum likelihood is ob-
tained under a misspeci ed missing data model. We further explore a likelihood
ratio statistic for testing the missing data mechanism in binary longitudinal data.
The empirical level and power of the test are investigated in small simulations.
We also present an example using some real data obtained from a longitudinal