Adeniyi Francis Fagbamigbe
University of Ibadan
Institution: University of Ibadan
Area of Research: Modeling association between bivariate censored outcomes: a case study of bipolar disorder
He holds a doctorate degree in Medical Statistics in addition to master’s degree in Medical Statistics, Project Development and Implementation as weel as MA in Monitoring and Evaluation and First Class Honours degree in Statistics. He has keen interest in Application of Statistics and Demography in Human Health. My research activities as a Medical Statistician have been shaped over the years by evolving needs to develop and apply appropriate statistical methodologies, to understand and answer pertinent questions on health outcomes and their determinants as well as chances of such outcomes, its inequality and prognostic factors. My main research focus is on the development, critiquing and application of survival analysis regression models to censored data with a bias for maternal and child health and sexual and reproductive health. The goal of my research activities is to inform policy framework through the prevention and management of morbidities and mortalities among these vulnerable sub-population-groups aimed at the relevant defunct millennium and the current sustainable development goals in developing countries. Evidence-based information through adequate statistical procedures is very essential in updating knowledge, solving health problems, providing facts to policymakers for decision making and interventions that can impact individual-level and population-level outcomes. This has motivated my research work within the medical circle to understand, provide and use appropriate statistical procedures in planning, implementation, analysis and dissemination of both clinical and public health studies. I have reviewed, developed and applied statistical methods to real-life and simulated data. My research activities in the development and application of survival analysis techniques started at my Master’s degree level with the development of a model for measuring the association between right-censored bivariate outcomes. The model employed survival analysis concept multivariate normal probability distribution, correlational concepts, transformation techniques, maximum likelihood estimation techniques and was applied to recurrence time of kidney infection among individuals using a catheter. The study found a significant correlation between the censored recurrence time of infection between the two kidneys. At the Ph.D. level, mathematical simulation procedures were engaged to validate the developed model after its application to the recurrence time of mania and depression among bipolar disorder patients in Nigeria. Findings from this model have been used by medical researchers. Additionally, I have used survival analysis techniques to explore the timing and drivers of uptake antenatal care services among pregnant women in Nigeria, the timing and prognostic factors of sexual debut among teenagers, timing and factors associated with demand for and uptake of modern contraceptives as well as duration and factors associated with postpartum abstinence among Nigerian women. It has also been extended to the analysis of menarche onset timing among Nigerian girls, survival analysis frailty modelling of unobserved heterogeneity in the determinants of under-five mortality in Nigeria, prognostic factors associated with the timing of first forced sexual act and first domestic violence after marriage among Africa women. Different survival analysis regression models including the Cox proportional hazard, parametric (Gamma, lognormal, Weibull and exponential) and Royston and Palmar flexible parametric survival models were used. Besides enabling public health researchers in my setting to embrace these techniques. The outcomes and conclusions of these studies led to evidence-based recommendations to local, and international policy-makers. I have taken up the rare challenges and opportunities presented by the evolution of data science within the last decade. These have further strengthened and widened my research focus through the incorporation of health data science into my mainstream research activities. In a set of collaborative research, I have adopted health data science techniques into big data analysis of multi-country data pooled from secondary data of up to 51 low- and middle-income countries. While using health data science techniques, I have used diverse statistical techniques including Spatio-temporal analysis, multilevel and hierarchical analysis, decomposition analysis and quantification of health inequalities using both Frequentist and Bayesian approaches. I have kept myself abreast of existing and new statistical methods, approaches and software to have deep know-how of evolving changes and dynamism in my area of specialization. The drive to be up-to-date with developments in Medical Statistics birthed massive collaborations and new skills to manage big health data. My long term research goal is to be an impacting Medical Statistician and Health Data Scientist within formidable research teams. My future research plans are to further strengthen my research focal areas, seek opportunities for collaborative, multidisciplinary research activities at both local and international levels.