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Cited by 479

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Non‐hierarchical logistic models and case‐only designs for assessing susceptibility in population‐based case‐control studies

Piegorsch, Walter W., Weinberg, Clarice R., Taylor, Jack A.·1994·Statistics in Medicine

Abstract

This article describes how genetic components of disease susceptibility can be evaluated in case‐control studies, where cases and controls are sampled independently from the population at large. Subjects are assumed unrelated, in contrast to studies of familial aggregation and linkage. The logistic model can be used to test collapsibility over phenotypes or genotypes, and to estimate interactions between environmental and genetic factors. Such interactions provide an example of a context where non‐hierarchical models make sense biologically. Also, if the exposure and genetic categories occur independently and the disease is rare, then analyses based only on cases are valid, and offer better precision for estimating gene‐environment interactions than those based on the full data.

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Metadata

Authors
Piegorsch, W. W., Weinberg, C. R., & Taylor, J. A.
Venue
Statistics in Medicine
Publisher
Wiley
Published
1994-01-30
Volume
13
Issue
2
Pages
153-162
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
OpenAlex
W2123178249
Cited by
479
References
2
FWCI
1.84
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Non‐hierarchical logistic models and case‐only designs for assessing susceptibility in population‐based case‐control studies. https://app.cochat.ai/papers/10.1002/sim.4780130206
The paper
Piegorsch, W. W., Weinberg, C. R., & Taylor, J. A. (1994). Non‐hierarchical logistic models and case‐only designs for assessing susceptibility in population‐based case‐control studies. Statistics in Medicine. https://doi.org/10.1002/sim.4780130206

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.