Analysis of longitudinal data with non-ignorable non-monotone missing values |
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Authors: | A. B. Troxel,D. P. Harrington,& S. R. Lipsitz |
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Affiliation: | Columbia Presbyterian Medical Center, New York, USA,;Harvard School of Public Health and Dana Farber Cancer Institute, Boston, USA |
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Abstract: | A full likelihood method is proposed to analyse continuous longitudinal data with non-ignorable (informative) missing values and non-monotone patterns. The problem arose in a breast cancer clinical trial where repeated assessments of quality of life were collected: patients rated their coping ability during and after treatment. We allow the missingness probabilities to depend on unobserved responses, and we use a multivariate normal model for the outcomes. A first-order Markov dependence structure for the responses is a natural choice and facilitates the construction of the likelihood; estimates are obtained via the Nelder–Mead simplex algorithm. Computations are difficult and become intractable with more than three or four assessments. Applying the method to the quality-of-life data results in easily interpretable estimates, confirms the suspicion that the data are non-ignorably missing and highlights the likely bias of standard methods. Although treatment comparisons are not affected here, the methods are useful for obtaining unbiased means and estimating trends over time. |
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Keywords: | Incomplete data Markov correlation Maximum likelihood Quality of life Repeated measurements |
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