Survival data are often clustered into groups, such as couples,
families, communities, and geographical regions. Observations from
same cluster usually share certain unobserved characteristics and
as a result tend to be correlated. In multivariate proportional
hazards model correlations among observations are considered. In
analysis if associations among observations are ignored, standard
error of the estimates of parameters of interest may be incorrect.
The parameters estimates yielded by the multivariate proportional
hazards model are very similar to those yielded by the standard
hazards model. Present research deals with the extension of the Cox
model that allows for heterogeneity due to omitted covariates using
frailty (random effect) approach, and there by uses a more general
class of mixed-modeling that estimates predictors via parametric
and non-parametric regression. In this study, Cox model and
multivariate proportional hazards model are used for analyzing
birth interval of Bangladesh using Bangladesh Demographic and
Health Survey (BDHS, 2004) data. This result of this study
indicates that, the unobserved cluster effect has a sizeable impact
on birth interval in Bangladesh.
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