Experts Examine Implications Of ‘Deep’ Statistical Uncertainty In TB Decision Making By Policymakers, Clinicians, Others
Center for Global Development: Managing “Deep” Uncertainty in Global Health: The Case of TB Testing
Rachel Cassidy, CGD non-resident fellow, and Charles Manski, board of trustees professor at Northwestern University, address the implications of statistical uncertainty for decision making in global health, examining the case of TB testing and treatment specifically. The authors write, “Policymakers and clinicians in global health often face considerable uncertainty when making decisions. Statistical uncertainty — arising from the fact that analysis and modeling typically use samples rather than whole populations — can be accounted for by placing confidence intervals on the estimated impacts of different policies and treatment regimes. However, ‘deep’ uncertainty — which may arise from issues including missing data or limited external validity from existing trials — poses a more fundamental challenge to decision-making. In a newly published article in The Proceedings of the National Academy of Sciences, we examine such ‘deep’ uncertainty in the context of a new diagnostic test for tuberculosis (TB), including discussions of diversification, and ask what a reasonable policy response might be for public health agencies combatting TB” (10/31).