ArticleStatistics in medicine2017
A comparison of statistical methods for the study of etiologic heterogeneity.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Modification of the Association Between Frequent Aspirin Use and Ovarian Cancer Risk: A Meta-Analysis Using Individual-Level Data From Two Ovarian Cancer Consortia.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2022Pooled it
- Efficiency for evaluation of disease etiologic heterogeneity in case-case and case-control studies.The international journal of biostatistics · 2025Article
- Assessing etiological heterogeneity for multinomial outcome with two-phase outcome-dependent sampling design.American journal of epidemiology · 2025Article
- Design of a Location-Based Case-Control Study of Built Environment Risk Factors for Pedestrian Fatalities in the U.S.American journal of epidemiology · 2024Article
- A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics.Biostatistics (Oxford, England) · 2021Article
- Validity of a method for identifying disease subtypes that are etiologically heterogeneous.Statistical methods in medical research · 2021Article
- Reflection on modern methods: causal inference considerations for heterogeneous disease etiology.International journal of epidemiology · 2021Article
- Risk factors for melanoma by anatomical site: an evaluation of aetiological heterogeneity.The British journal of dermatology · 2021Article
- Integration of microbiology, molecular pathology, and epidemiology: a new paradigm to explore the pathogenesis of microbiome-driven neoplasms.The Journal of pathology · 2019Review
- Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine.Gut · 2018Review
- Ten Essential Practices for Developing or Reforming a Biostatistics Core for a NCI Designated Cancer Center.JNCI cancer spectrum · 2018Review
- The clinical trajectory of emerging bipolar disorder among the high-risk offspring of bipolar parents: current understanding and future considerations.International journal of bipolar disorders · 2017Review
- Integration of pharmacology, molecular pathology, and population data science to support precision gastrointestinal oncology.NPJ precision oncology · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Cancer epidemiologic research has traditionally been guided by the premise that certain diseases share an underlying etiology, or cause. However, with the rise of molecular and genomic profiling, attention has increasingly focused on identifying subtypes of disease. As subtypes are identified, it is natural to ask the question of whether they share a common etiology or in fact arise from distinct sets of risk factors. In this context, epidemiologic questions of interest include (1) whether a risk factor of interest has the same effect across all subtypes of disease and (2) whether risk factor effects differ across levels of each individual tumor marker of which the subtypes are comprised. A number of statistical models have been proposed to address these questions. In an effort to determine the similarities and differences among the proposed methods, and to identify any advantages or disadvantages, we use a simplified data example to elucidate the interpretation of model parameters and available hypothesis tests, and we perform a simulation study to assess bias in effect size, type I error, and power. The results show that when the number of tumor markers is small enough that the cross-classification of markers can be evaluated in the traditional polytomous logistic regression framework, then the statistical properties are at least as good as the more complex modeling approaches that have been proposed. The potential advantage of more complex methods is in the ability to accommodate multiple tumor markers in a model of reduced parametric dimension.
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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.