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Combining Population and Study Data for Inference on Event Rates

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  • Christoph Rothe

Abstract

This note considers the problem of conducting statistical inference on the share of individuals in some subgroup of a population that experience some event. The specific complication is that the size of the subgroup needs to be estimated, whereas the number of individuals that experience the event is known. The problem is motivated by the recent study of Streeck et al. (2020), who estimate the infection fatality rate (IFR) of SARS-CoV-2 infection in a German town that experienced a super-spreading event in mid-February 2020. In their case the subgroup of interest is comprised of all infected individuals, and the event is death caused by the infection. We clarify issues with the precise definition of the target parameter in this context, and propose confidence intervals (CIs) based on classical statistical principles that result in good coverage properties.

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  • Christoph Rothe, 2020. "Combining Population and Study Data for Inference on Event Rates," Papers 2005.06769, arXiv.org.
    • Handle: RePEc:arx:papers:2005.06769
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    Cited by:

    1. Jörg Stoye, 2022. "Bounding infection prevalence by bounding selectivity and accuracy of tests: with application to early COVID-19 [False-negative results of initial RT-PCR assays for COVID-19: a systematic review]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 1-14.

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