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Obtaining marginal estimates from conditional categorical repeated measurements models with missing data
Author(s) -
Lindsey J. K.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(20000330)19:6<801::aid-sim393>3.0.co;2-r
Subject(s) - categorical variable , marginal model , missing data , statistics , econometrics , mathematics , data set , computer science , markov chain , marginal likelihood , longitudinal data , markov model , set (abstract data type) , maximum likelihood , data mining , regression analysis , programming language
The most commonly used models for categorical repeated measurement data are log‐linear models. Not only are they easy to fit with standard software but they include such useful models as Markov chains and graphical models. However, these are conditional models and one often also requires the marginal probabilities of responses, for example, at each time point in a longitudinal study. Here a simple method of matrix manipulation is used to derive the maximum likelihood estimates of the marginal probabilities from any such conditional categorical repeated measures model. The technique is applied to the classical Muscatine data set, taking into account the dependence of missingness on previous observed values, as well as serial dependence and a random effect. Copyright © 2000 John Wiley & Sons, Ltd.