Description: Sensitivity analyses that quantify the robustness of inferences to concerns about omitted variables and other sources of bias. Questions? Issues? Suggestions? Reach out through the KounFound-It! Google Group. Latest News Space still available for hybrid ICPSR course (July 8-12, 2024): “Sensitivity Analysis: Quantifying the Robustness of Inferences to Alternative Factors or Data." (Modalities: virtual or in-person in Ann Arbor, MI, with asynchronous access to materials) Most social scientists need to conside
Welcome to our discussion about sensitivity analysis. All of the assumptions of statistical analysis rarely hold. So the challenge for the pragmatist is to understand when evidence is strong enough to support action. That’s where sensitivity analysis comes in – so we can understand how robust our inferences are to challenges to our assumptions. One example is statements such as “XX% of the estimated effect would have to be due to bias to change your inference about the effect.
As those who work in public policy and health, we seek to help a broad range of people, with a broad range of statistical backgrounds, interpret uncertainty about public health findings regarding COVID-19. We observe that currently, there is little common language for expressing uncertainty. Consider Anthony Fauci’s quote (as in Healio on April 29): “The trial, which began Feb. 21 this year, compared remdesivir with placebo in more than \(1,000\) patients.
The first report of a randomized trial regarding hydroxychloroquine (HCQ) came from a study conducted at the Renmin Hospital of Wuhan University. The table below shows the association between treatment (HCQ vs conventional) and condition (improved vs exacerbated/ucnhanged). For Table 1, \(χ^2= 4.7\), \(p = 0.03\), and the authors concluded that HCQ was efficacious. Table 1. Association between hydroxychloroquine (HCQ) vs Conventional Treatments and Pneumonia on Chest CT To quantify the robustness of the inf