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The Analysis of Group Truncated Binary Data with Random Effects: Injury Severity in Motor Vehicle Accidents
Author(s) -
Barry S. C.,
O'Neill T. J.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00443.x
Subject(s) - statistics , binary data , random effects model , binary number , data set , group (periodic table) , biometrics , set (abstract data type) , generalized linear mixed model , mathematics , computer science , medicine , artificial intelligence , arithmetic , chemistry , meta analysis , organic chemistry , programming language
Summary. The analysis of group truncated binary data has been previously considered by O'Neill and Barry (1995b, Biometrics 51 , 533–541), where the analysis assumed that responses within each group were independent. In this paper, we consider the analysis of such data when there is group‐level heterogeneity. A generalized linear mixed model is hypothesized to model the response and maximum likelihood estimates are derived for the truncated case. A score test is derived to test for heterogeneity. Finally, the method is applied to a set of traffic accident data.