The analysis of group truncated binary data with random effects: Injury severity in motor vehicle accidents

S. C. Barry; T. J. O'Neill

doi:10.1111/j.0006-341X.2000.00443.x

The analysis of group truncated binary data with random effects: Injury severity in motor vehicle accidents

S. C. Barry^*, T. J. O'Neill

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	443-450
Number of pages	8
Journal	Biometrics
Volume	56
Issue number	2
DOIs	https://doi.org/10.1111/j.0006-341X.2000.00443.x
Publication status	Published - Jun 2000

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title = "The analysis of group truncated binary data with random effects: Injury severity in motor vehicle accidents",

abstract = "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.",

keywords = "Group truncation, Road safety, Truncated regression, Truncation",

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year = "2000",

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N2 - 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.

AB - 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.

KW - Group truncation

KW - Road safety

KW - Truncated regression

KW - Truncation

UR - https://www.scopus.com/pages/publications/0033917086

U2 - 10.1111/j.0006-341X.2000.00443.x

DO - 10.1111/j.0006-341X.2000.00443.x

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VL - 56

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JO - Biometrics

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ER -

The analysis of group truncated binary data with random effects: Injury severity in motor vehicle accidents

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