The analysis of group truncated binary data with random effects: injury severity in motor vehicle accidents.
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.
Barr
S C
SC
Department of Statistics and Econometrics, Australian National University, Canberra ACT. simon.barry@brs.gov.au
O'Neill
T J
TJ
D000063:Accidents, Traffic / Q000401:mortality / Q000706:statistics & numerical data*; D001315:Australia / Q000453:epidemiology; D001336:Automobiles; D001699:Biometry / Q000379:methods; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D012720:Severity of Illness Index; D014947:Wounds and Injuries / Q000453:epidemiology / Q000401:mortality / Q000503:physiopathology*