ReviewInfectious Disease Modelling2023
Hypothesis testing of Poisson rates in COVID-19 offspring distributions.
Review in Infectious Disease Modelling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
1 author.
Funding
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Abstract
In the present study, we undertake the task of hypothesis testing in the context of Poisson-distributed data. The primary objective of our investigation is to ascertain whether two distinct sets of discrete data share the same Poisson rate. We delve into a comprehensive review and comparative analysis of various frequentist and Bayesian methodologies specifically designed to address this problem. Among these are the conditional test, the likelihood ratio test, and the Bayes factor. Additionally, we employ the posterior predictive
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.