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An Alternative Stratified Cox Model for Correlated Variables in Infant Mortality Pages 23-31
K.A. Adeleke and A.A. Abiodun


Published: 08 July 2019

Abstract: Often in epidemiological research, introducing a stratified Cox model can account for the existence of interactions of some inherent factors with some major/noticeable factors. This paper aims at modelling correlated variables in infant mortality with the existence of some inherent factors affecting the infant survival function. A Stratified Cox model is proposed with a view to taking care of multi-factor-level that has interactions with others. This, however, is used as a tool to model infant mortality data from Nigeria Demographic and Health Survey (NDHS) with g-level-factor (Tetanus, Polio and Breastfeeding) having correlations with main factors (Sex, infant Size and Mode of Delivery). Asymptotic properties of partial likelihood estimators of regression parameters are also studied via simulation. The proposed models are tested via data and it shows good fit and performs differently depending on the levels of the interaction of the strata variable Z*. An evidence that the baseline hazard functions and regression coefficients are not the same from stratum to stratum provides a gain in information as against the usage of the Cox model. Simulation result shows that the present method produces better estimates in terms of bias, lower standard errors, and or mean square errors.

Keywords: Stratified Cox, Semiparametric model, infant mortality, multifactor-level, confounding variables.


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