Abstract
We consider analysis of multivariate data with a monotone pattern of missing values, where the missingness depends on the underlying value of the missing variable. Maximum likelihood and estimating-equation- based methods, based on selection models, require specifying the functional form of the missing-data mechanism. Pattern-mixture models are useful for multivariate monotone missing data with two patterns but difficult to generalize to data with more than two patterns. Pseudolikelihood selection models can obtain consistent estimates of complete-data model parameters without specifying the missing-data mechanism. We extend this method to a class of more general missing-data mechanisms and illustrate its utility using data from a schizophrenia trial.
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Tang, G., Little, R.J.A., Raghunathan, T.E. (2004). Analysis of Multivariate Monotone Missing Data by A Pseudolikelihood Method. In: Lin, D.Y., Heagerty, P.J. (eds) Proceedings of the Second Seattle Symposium in Biostatistics. Lecture Notes in Statistics, vol 179. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9076-1_3
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DOI: https://doi.org/10.1007/978-1-4419-9076-1_3
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