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A Shared Frailty Model for Left-Truncated and Right-Censored Under-Five Child Mortality Data in South Africa

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  • Tshilidzi Benedicta Mulaudzi

    (Department of Mathematical and Computational Sciences, University of Venda, Thohoyandou 0950, South Africa)

  • Yehenew Getachew Kifle

    (Department of Mathematics and Statistics, University of Maryland, Baltimore County, MD 21250, USA)

  • Roel Braekers

    (Data Science Institute, Center for Statistics, Hasselt University, 3500 Diepenbeek, Belgium)

Abstract

Many African nations continue to grapple with persistently high under-five child mortality rates, particularly those situated in the Sub-Saharan region, including South Africa. A multitude of socio-economic factors are identified as key contributors to the elevated under-five child mortality in numerous African nations. This research endeavors to investigate various factors believed to be associated with child mortality by employing advanced statistical models. This study utilizes child-level survival data from South Africa, characterized by left truncation and right censoring, to fit a Cox proportional hazards model under the assumption of working independence. Additionally, a shared frailty model is applied, clustering children based on their mothers. Comparative analysis is performed between the results obtained from the shared frailty model and the Cox proportional hazards model under the assumption of working independence. Within the scope of this analysis, several factors stand out as significant contributors to under-five child mortality in the study area, including gender, birth province, birth year, birth order, and twin status. Notably, the shared frailty model demonstrates superior performance in modeling the dataset, as evidenced by a lower likelihood cross-validation score compared to the Cox proportional hazards model assuming independence. This improvement can be attributed to the shared frailty model’s ability to account for heterogeneity among mothers and the inherent association between siblings born to the same mother, ultimately enhancing the quality of the study’s conclusions.

Suggested Citation

  • Tshilidzi Benedicta Mulaudzi & Yehenew Getachew Kifle & Roel Braekers, 2023. "A Shared Frailty Model for Left-Truncated and Right-Censored Under-Five Child Mortality Data in South Africa," Stats, MDPI, vol. 6(4), pages 1-11, October.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:4:p:63-1018:d:1254417
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    References listed on IDEAS

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    1. Roland Pongou, 2013. "Why Is Infant Mortality Higher in Boys Than in Girls? A New Hypothesis Based on Preconception Environment and Evidence From a Large Sample of Twins," Demography, Springer;Population Association of America (PAA), vol. 50(2), pages 421-444, April.
    2. Roland Pongou, 2013. "Erratum to: Why Is Infant Mortality Higher in Boys Than in Girls? A New Hypothesis Based on Preconception Environment and Evidence From a Large Sample of Twins," Demography, Springer;Population Association of America (PAA), vol. 50(2), pages 445-446, April.
    3. Klara Goethals & Paul Janssen & Luc Duchateau, 2008. "Frailty models and copulas: similarities and differences," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(9), pages 1071-1079.
    4. Hui Zhang & Douglas E. Schaubel & John D. Kalbfleisch, 2011. "Proportional Hazards Regression for the Analysis of Clustered Survival Data from Case–Cohort Studies," Biometrics, The International Biometric Society, vol. 67(1), pages 18-28, March.
    5. Legrand, Catherine, 2021. "Advanced Survival Models," LIDAM Reprints ISBA 2021015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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