154 research outputs found

    Drs. Gilboa-Schechtman and Foa reply

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    Odds Ratio, Relative Risk, Absolute Risk Reduction, and the Number Needed to Treat—Which of These Should We Use?

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    AbstractIntroductionStatistical analyses of data and making sense of medical data have received much attention in the medical literature, but nevertheless have caused confusion among practitioners. Each researcher provides a different method for comparing treatments. For example, when the end point is binary, such as disease versus no disease, the common measures are odds ratios, relative risk, relative risk reduction, absolute risk reduction, and the number needed to treat. The question faced by the practitioner is then: Which one will help me in choosing the best treatment for my patient?MethodsThe purpose of this paper is to illustrate, using examples, how each measure is used, what it means, and what are its advantages and disadvantages.ResultsSome pairs of measures present equivalent information. Furthermore, it is shown that different measures result in different impressions.ConclusionIt is recommended that researchers report both a relative and an absolute measure and present these with appropriate confidence intervals

    The role of statisticians in the response to COVID-19 in Israel: a holistic point of view

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    The COVID-19 pandemic cast a dramatic spotlight on the use of data as a fundamental component of good decision-making. Evaluating and comparing alternative policies required information on concurrent infection rates and insightful analysis to project them into the future. Statisticians in Israel were involved in these processes early in the pandemic in some silos as an ad-hoc unorganized effort. Informal discussions within the statistical community culminated in a roundtable, organized by three past presidents of the Israel Statistical Association, and hosted by the Samuel Neaman Institute in April 2021. The meeting was designed to provide a forum for exchange of views on the profession’s role during the COVID-19 pandemic, and more generally, on its influence in promoting evidence-based public policy. This paper builds on the insights and discussions that emerged during the roundtable meeting and presents a general framework, with recommendations, for involving statisticians and statistics in decision-making.</p

    Suggestions for Further Research

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    Decompositions of the GMD

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