Multivariate Logistic Models for Incomplete Binary Responses
作者:
GarrettM. Fitzmaurice,
NanM. Laird,
GwendolynE. P. Zahner,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 433
页码: 99-108
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476667
出版商: Taylor & Francis Group
关键词: Binary response;EM algorithm;Marginal models;Missing data
数据来源: Taylor
摘要:
In this article we describe a likelihood-based regression model appropriate for analyzing incomplete multivariate binary responses. We focus on “marginal models”; that is, models where the marginal mean or expectation of the binary response is related to a set of covariates. The association between the binary responses is modeled in terms ofconditionallog odds ratios. When the nonresponse mechanism isignorable, it is not necessary to specify a nonresponse model, and valid inferences can be obtained provided that the likelihood for the responses has been correctly specified. But when the nonresponse mechanism isnonignorable, valid inferences can only be obtained by incorporating a model for nonresponse. An unresolved issue with nonignorable models concerns the identifiability of the parameters. So far, no general and practically useful necessary and sufficient conditions for identifiability are available. Here we suggest some simple procedures for examining the identifiability status of nonignorable models when the response variable is discrete. Finally, we present results for an analysis of multiple informant data from the New Haven Child Survey and the Eastern Connecticut Child Survey.
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