Theory–simulation–application framework to enhance biostatistical capacity for HIV and cancer data analysis in sub-Saharan Africa: lessons learnt from Tanzania

Abstract

Enhancing biostatistical capacity in sub-Saharan Africa (SSA) is imperative for the efficient analysis of routinely collected health data and for clinical and public health decision-making. We describe the experiences and lessons learnt from implementing advanced statistical capacity-building workshops that integrate theory, simulation, and application in Tanzania, in partnership with the International Biometric Society Tanzania Region. We address four key needs in our African institutional settings: (1) limited advanced statistical skills for analysing routinely collected data, (2) insufficient capacity to ethically harmonise and link sensitive data such as HIV clinic records, cancer registries, and other electronic health records, (3) challenges with non-interoperable data systems, and (4) a shortage of senior mentorship for early- and mid-career researchers with advanced statistical expertise. We analysed survey feedback collected before and after the workshops and group discussions, as well as the outcomes of our interdisciplinary network activities. Our focus was on improving data analytical skills through advanced statistical methods, data linkages, interdisciplinary teamwork, and mentorship opportunities, and on their implications for sustainable biostatistics capacity-building in SSA. We urge biostatisticians in SSA to view themselves not only as practitioners but also as leaders who can develop and assess novel statistical methods, analyse potential biases, pose relevant epidemiological questions, and challenge existing assumptions. Our experiences in learning and teaching suggest that interdisciplinary biostatistics workshops are effective entry points for a broader capacity-building strategy, offering instruction ranging from basic to advanced statistical methods when integrated into ongoing training and mentorship structures.