ArticleScientific reports2016
Complementary Log Regression for Sufficient-Cause Modeling of Epidemiologic Data.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- A Study on Risk Factors for Bovine Tuberculosis in the Disease-Free Regions of Italy.Pathogens (Basel, Switzerland) · 2026Article
- Family relationship, environmental, and cognitive correlates of adolescents' drug use in Cameroon: a school-based cross-sectional study across three secondary schools.BMC public health · 2026Article
- Article
- Coverage-level and predictors of maternity continuum of care in Nigeria: implications for maternal, newborn and child health programming.BMC pregnancy and childbirth · 2023Article
- Response to Biologics During the First Six Months of Therapy in Biologic-naïve Patients with Psoriasis Predicts Risk of Disease Flares: A Danish Nationwide Study.Acta dermato-venereologica · 2021Article
- A self-consistent probabilistic formulation for inference of interactions.Scientific reports · 2020Article
- A novel method for controlling unobserved confounding using double confounders.BMC medical research methodology · 2020Article
- Risk factors for lower limb lymphedema in gynecologic cancer patients after initial treatment.International journal of clinical oncology · 2020Article
- Testing for Sufficient-Cause Interactions in Case-Control Studies of Non-Rare Diseases.Scientific reports · 2018Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
The logistic regression model is the workhorse of epidemiological data analysis. The model helps to clarify the relationship between multiple exposures and a binary outcome. Logistic regression analysis is readily implemented using existing statistical software, and this has contributed to it becoming a routine procedure for epidemiologists. In this paper, the authors focus on a causal model which has recently received much attention from the epidemiologic community, namely, the sufficient-component cause model (causal-pie model). The authors show that the sufficient-component cause model is associated with a particular 'link' function: the complementary log link. In a complementary log regression, the exponentiated coefficient of a main-effect term corresponds to an adjusted 'peril ratio', and the coefficient of a cross-product term can be used directly to test for causal mechanistic interaction (sufficient-cause interaction). The authors provide detailed instructions on how to perform a complementary log regression using existing statistical software and use three datasets to illustrate the methodology. Complementary log regression is the model of choice for sufficient-cause analysis of binary outcomes. Its implementation is as easy as conventional logistic regression.
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