ArticleJournal of applied statistics2023
A novel outlier statistic in multivariate survival models and its application to identify unusual under-five mortality sub-districts in Malawi.
Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Analysis of outlier villages with high under-five mortality rates in Malawi using mixed-effects logistic regression model residuals.BMC public health · 2025Article
- Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi.BMC public health · 2024Article
- Article
- A bivariate Poisson regression to analyse impact of outlier women on correlation between female schooling and fertility in Malawi.BMC women's health · 2024Article
- Bivariate logistic regression model diagnostics applied to analysis of outlier cancer patients with comorbid diabetes and hypertension in Malawi.Scientific reports · 2023Article
- Analysing outlier communities to child birth weight outcomes in Malawi: application of multinomial logistic regression model diagnostics.BMC pediatrics · 2022Article
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2 authors.
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Abstract
Although under-five mortality (U5M) rates have declined worldwide, many countries in sub-Saharan Africa still have much higher rates. Detection of subnational areas with unusually higher U5M rates could support targeted high impact child health interventions. We propose a novel group outlier detection statistic for identifying areas with extreme U5M rates under a multivariate survival data model. The performance of the proposed statistic was evaluated through a simulation study. We applied the proposed method to an analysis of child survival data in Malawi to identify sub-districts with unusually higher or lower U5M rates. The simulation study showed that the proposed outlier statistic can detect unusual high or low mortality groups with a high accuracy of at least 90%, for datasets with at least 50 clusters of size 80 or more. In the application, at most 7 U5M outlier sub-districts were identified, based on the best fitting model as measured by the Akaike information criterion (AIC).
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