Research Areas

Diagnostic statistics, Multivariate linear and non-linear models, Time-to-event data analysis, and general Biostatistics,

Profile

Dr. Tsirizani Kaombe is a practicing statistician serving as senior lecturer, consultant, and researcher in the Department of Mathematical Sciences at the University of Malawi. His recent publications are available at https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Tsirizani+M.+Kaombe&btnG=. He holds a Ph.D. degree in Biostatistics obtained from the University of Malawi-Chancellor College in the year 2020. His Ph.D. thesis was titled "Identifying outlying and influential clusters in the analysis of multivariate survival data", and Prof. Samuel Manda of the University of Pretoria supervised it. Dr. Kaombe's research interests are in Diagnostic statistics; Multivariate linear and non-linear models; Time-to-event data analysis; and their application. He is a journal reviewer of four journals, namely: BMC Public Health, BMC Research Notes, BMC Infectious Diseases, and BMJ Open journals. He is a member of the International Biometric Society (IBS), and Southern Africa Mathematical Sciences Association (SAMSA). He teaches various undergraduate and postgraduate courses in statistics, including Distribution theory; Statistical Quality Control; Foundations of Probability and Statistics; Hypothesis testing; Generalized Linear Models; Experimental designs and analysis; and Survival data analysis. He has successfully supervised 5 MSC theses in Biostatistics since 2016 and is currently supervising 5 more. He has internally examined 5 Master's theses for the MSc. in Biostatistics and MA. in Economics at the University of Malawi, and externally examined one Ph.D. thesis in Statistics and one MSc. thesis in Statistics, both for the University of Cape Town. He has worked as a consultant in evaluating health, education, and social sciences projects. He also has expertise in designing strategic plans and monitoring and evaluation systems for organizations and projects. In community engagement activities, he currently serves as Chairperson of the Board of Directors for the Centre for Democracy and Elections (CEDE), a registered Civil Society Organization (CSO) in Malawi that focuses on democracy advocacy and monitoring.

Publications

• Journal Article
  Kaombe, T. M. (2024). A bivariate Poisson regression to analyse impact of outlier women on correlation between female schooling and fertility in Malawi. BMC Women's Health, 24(1), 1-18. (2024)
  https://bmcwomenshealth.biomedcentral.com/articles/10.1186/s12905-024-02891-w

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• Journal Article
  Hamuza, G.A., Singogo, E. & Kaombe, T.M. Application of multivariate binary logistic regression grouped outlier statistics and geospatial logistic model to identify villages having unusual health-seeking habits for childhood malaria in Malawi. Malar J 23, 246 (2024). https://doi.org/10.1186/s12936-024-05070-2 (2024)
  https://doi.org/10.1186/s12936-024-05070-2

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• Journal Article
  Mponda, E., Kaombe, T. Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi. BMC Public Health 24, 2285 (2024). https://doi.org/10.1186/s12889-024-19816-9 (2024)
  https://doi.org/10.1186/s12889-024-19816-9

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• Journal Article
  Kaombe, T. M., Banda, J. C., Hamuza, G. A., and Muula, A. S. (2023). Bivariate logistic regression model diagnostics applied to analysis of outlier cancer patients with comorbid diabetes and hypertension in malawi. Scientific Reports, 13(1):8340. (2023)
  https://www.nature.com/articles/s41598-023-35475-z

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• Journal Article
  Kaombe, T. M. and Hamuza, G. A. (2023). Impact of ignoring sampling design in the prediction of binary health outcomes through logistic regression: evidence from malawi demographic and health survey under-five mortality data; 2000-2016. BMC Public Health, 23(1):1–12. (2023)
  https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-023-16544-4

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• Journal Article
  Tsirizani M. Kaombe and Samuel O.M. Manda (2022): A Novel Outlier Statistic in Multivariate Survival Models and its Application to Identify Unusual Under-Five Mortality Sub-Districts in Malawi, Journal of Applied Statistics, DOI: 10.1080/02664763.2022.2043255 (2022)
  https://www.tandfonline.com/doi/full/10.1080/02664763.2022.2043255

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• Journal Article
  Natasha Sakala and Tsirizani M. Kaombe (2022). Analysing outlier communities to child birth weight outcomes in Malawi: application of multinomial logistic regression model diagnostics. BMC Pediatrics, 22:682, https://doi.org/10.1186/s12887-022-03742-z (2022)
  https://bmcpediatr.biomedcentral.com/articles/10.1186/s12887-022-03742-z

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• Book Chapters
  Tsirizani M. Kaombe and Samuel O.M. Manda (2022). Identifying outlying and influential clusters in multivariate survival data models. In: Chen, DG., Manda, S.O.M., Chirwa, T.F. (eds) Modern Biostatistical Methods for Evidence-Based Global Health Research. Emerging Topics in Statistics and Biostatistics. Springer, Cham. https://doi.org/10.1007/978-3-031-11012-2_15 (2022)
  https://link.springer.com/chapter/10.1007/978-3-031-11012-2_15

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• Journal Article
  Tsirizani M. Kaombe & Samuel O.M. Manda (2021): Detecting influential data in multivariate survival models, Communications in Statistics - Theory and Methods, DOI: 10.1080/03610926.2021.1982983 (2021)
  https://www.tandfonline.com/doi/full/10.1080/03610926.2021.1982983

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