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. 2023 May 23;13(1):8340.
doi: 10.1038/s41598-023-35475-z.

Bivariate logistic regression model diagnostics applied to analysis of outlier cancer patients with comorbid diabetes and hypertension in Malawi

Tsirizani M Kaombe  1 , Jonathan Chiwanda Banda  2 , Gracious A Hamuza  3 , Adamson S Muula  4   2
Affiliations

Bivariate logistic regression model diagnostics applied to analysis of outlier cancer patients with comorbid diabetes and hypertension in Malawi

Tsirizani M Kaombe et al. Sci Rep. .

Abstract

The joint occurrence of diabetes and hypertension conditions in a patient is common. The two diseases share a number of risk factors, and are hence usually modelled concurrently using bivariate logistic regression. However, the postestimation assessment for the model, such as analysis of outlier observations, is seldom carried out. In this article, we apply outlier detection methods for multivariate data models to study characteristics of cancer patients with joint outlying diabetes and hypertension outcomes observed from among 398 randomly selected cancer patients at Queen Elizabeth and Kamuzu Central Hospitals in Malawi. We used R software version 4.2.2 to perform the analyses and STATA version 12 for data cleaning. The results showed that one patient was an outlier to the bivariate diabetes and hypertension logit model. The patient had both diabetes and hypertension and was based in rural area of the study population, where it was observed that comorbidity of the two diseases was uncommon. We recommend thorough analysis of outlier patients to comorbid diabetes and hypertension before rolling out interventions for managing the two diseases in cancer patients to avoid misaligned interventions. Future research could perform the applied diagnostic assessments for the bivariate logit model on a wider and larger dataset of the two diseases.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Outliers to marginal outcomes in non-exchangeable bivariate logit model, Malawi cancer patients data. Source Researcher.
Figure 2
Figure 2
Outliers to marginal outcomes in exchangeable bivariate logit model, Malawi cancer patients data. Source Researcher.
Figure 3
Figure 3
Joint outliers to the non-exchangeable bivariate logit model, Malawi cancer patients data. Source Researcher.
Figure 4
Figure 4
Joint outliers to the non-exchangeable bivariate logit model, Malawi cancer patients data. Source Researcher.
Figure 5
Figure 5
Joint outliers to the exchangeable bivariate logit model, Malawi cancer patients data. Source Researcher.
Figure 6
Figure 6
Joint outliers to the non-exchangeable and exchangeable bivariate logit models, Malawi cancer patients data. Source Researcher.
See this image and copyright information in PMC

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