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Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach
Author(s) -
Yi Grace Y.,
Thompson Mary E.
Publication year - 2005
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5540330102
Subject(s) - longitudinal data , humanities , covariate , mathematics , statistics , computer science , philosophy , data mining
Abstract Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.