ArticleBMC medical research methodology2018
Odds ratios from logistic, geometric, Poisson, and negative binomial regression models.
Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
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- A comparison of statistical methods for modeling count data with an application to hospital length of stay.BMC medical research methodology · 2022Article
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- Engaging social media users with attitudinal messages during health crisis communication.Lingua. International review of general linguistics. Revue internationale de linguistique generale · 2022Article
- Differential risk for COVID-19 in the first wave of the disease among Spaniards and migrants from different areas of the world living in Spain.Revista clinica espanola · 2021Article
- SARS-CoV-2 and work-related transmission: results of a prospective cohort of airport workers, 2020.Revista brasileira de medicina do trabalho : publicacao oficial da Associacao Nacional de Medicina do Trabalho-ANAMT · 2021Article
- Limitations of Only Reporting the Odds Ratio in the Age of Precision Medicine: A Deterministic Simulation Study.Frontiers in medicine · 2021Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundThe odds ratio (OR) is used as an important metric of comparison of two or more groups in many biomedical applications when the data measure the presence or absence of an event or represent the frequency of its occurrence. In the latter case, researchers often dichotomize the count data into binary form and apply the well-known logistic regression technique to estimate the OR. In the process of dichotomizing the data, however, information is lost about the underlying counts which can reduce the precision of inferences on the OR.
methodsWe propose analyzing the count data directly using regression models with the log odds link function. With this approach, the parameter estimates in the model have the exact same interpretation as in a logistic regression of the dichotomized data, yielding comparable estimates of the OR. We prove analytically, using the Fisher information matrix, that our approach produces more precise estimates of the OR than logistic regression of the dichotomized data. We also show the gains in precision using simulation studies and real-world datasets. We focus on three related distributions for count data: geometric, Poisson, and negative binomial.
resultsIn simulation studies, confidence intervals for the OR were 56-65% as wide (geometric model), 75-79% as wide (Poisson model), and 61-69% as wide (negative binomial model) as the corresponding interval from a logistic regression produced by dichotomizing the data. When we analyzed existing datasets using our approach, we found that confidence intervals for the OR could be up to 64% shorter (36% as wide) compared to if the data had been dichotomized and analyzed using logistic regression.
conclusionsMore precise estimates of the OR can be obtained directly from the count data by using the log odds link function. This analytic approach is easy to implement in software packages that are capable of fitting generalized linear models or of maximizing user-defined likelihood functions.
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