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Presence of social network among the respondents in a survey may have an influence on the patterns of response. Latent class analysis identifies hidden subgroups in the respondents in a survey and simplifies the structure of heterogeneity amongst them. In the present study, a new model of social network-informed multiple group latent class analysis is developed by introducing a parameter measuring the influence of a social network on a respondent. For suitable application of the proposed model, data from 75 Indian villages with detailed demographic characteristics and a diverse social and economic network are used. The present study examined the methodological aspect of the proposed model of multiple group latent class analysis adjusted for the impact of social network. [ABSTRACT FROM AUTHOR]

In this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency. [ABSTRACT FROM AUTHOR]