The DOI matches its Crossref record.
Sample size requirements for matched case‐control studies of gene–environment interaction
Abstract
Consideration of gene–environment (G×E) interaction is becoming increasingly important in the design of new epidemiologic studies. We present a method for computing required sample size or power to detect G×E interaction in the context of three specific designs: the standard matched case‐control; the case‐sibling, and the case‐parent designs. The method is based on computation of the expected value of the likelihood ratio test statistic, assuming that the data will be analysed using conditional logistic regression. Comparisons of required sample sizes indicate that the family‐based designs (case‐sibling and case‐parent) generally require fewer matched sets than the case‐control design to achieve the same power for detecting a G×E interaction. The case‐sibling design is most efficient when studying a dominant gene, while the case‐parent design is preferred for a recessive gene. Methods are also presented for computing sample size when matched sets are obtained from a stratified population, for example, when the population consists of multiple ethnic groups. A software program that implements the method is freely available, and may be downloaded from the website http://hydra.usc.edu/gxe. Copyright © 2002 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Gene–environment interactions in human diseases
Hunter, D. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1578 · Cited by 1,132
VanderWeele, T. J., & Knol, M. J. (2014) · Epidemiologic Methods · doi:10.1515/em-2013-0005 · Cited by 1,127
Statistical power and significance testing in large-scale genetic studies
Sham, P. C., & Purcell, S. M. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3706 · Cited by 573
Exploiting Gene-Environment Interaction to Detect Genetic Associations
Kraft, P., Yen, Y.-C., Stram, D. O., Morrison, J., & Gauderman, W. J. (2007) · Human Heredity · doi:10.1159/000099183 · Cited by 439
Self-citationYao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B. B., Laser, A., Maranville, J. C., Wu, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431
The Tohoku Medical Megabank Project: Design and Mission
Kuriyama, S., Yaegashi, N., Nagami, F., Arai, T., Kawaguchi, Y., Osumi, N., Sakaida, M., Suzuki, Y., Nakayama, K., Hashizume, H., et al. (2016) · Journal of Epidemiology · doi:10.2188/jea.je20150268 · Cited by 359
Genomewide association for schizophrenia in the CATIE study: results of stage 1
Sullivan, P. F., Lin, D., Tzeng, J.-Y., van den Oord, E., Perkins, D., Stroup, T. S., Wagner, M., Lee, S., Wright, F. A., Zou, F., et al. (2008) · Molecular Psychiatry · doi:10.1038/mp.2008.25 · Cited by 355
Soria, V., Martínez-Amorós, È., Escaramís, G., Valero, J., Pérez-Egea, R., García, C., Gutiérrez-Zotes, A., Puigdemont, D., Bayés, M., Crespo, J. M., et al. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2009.230 · Cited by 351
Evaluating historical candidate genes for schizophrenia
Farrell, M. S., Werge, T., Sklar, P., Owen, M. J., Ophoff, R. A., O'Donovan, M. C., Corvin, A., Cichon, S., & Sullivan, P. F. (2015) · Molecular Psychiatry · doi:10.1038/mp.2015.16 · Cited by 315
Association between the oxytocin receptor gene and amygdalar volume in healthy adults
Inoue, H., Yamasue, H., Tochigi, M., Abe, O., Liu, X., Kawamura, Y., Takei, K., Suga, M., Yamada, H., Rogers, M. A., et al. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.07.019 · Cited by 164
Saus, E., Soria, V., Escaramís, G., Vivarelli, F., Crespo, J. M., Kagerbauer, B., Menchón, J. M., Urretavizcaya, M., Gratacòs, M., & Estivill, X. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq316 · Cited by 163
Moore, C. M., Jacobson, S. A., & Fingerlin, T. E. (2019) · Human Heredity · doi:10.1159/000508558 · Cited by 84
Genetic Determinants for Gestational Diabetes Mellitus and Related Metabolic Traits in Mexican Women
Huerta-Chagoya, A., Vázquez-Cárdenas, P., Moreno-Macías, H., Tapia-Maruri, L., Rodríguez-Guillén, R., López-Vite, E., García-Escalante, G., Escobedo-Aguirre, F., Parra-Covarrubias, A., Cordero-Brieño, R., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0126408 · Cited by 72
Blein, S., Berndt, S., Joshi, A. D., Campa, D., Ziegler, R. G., Riboli, E., Cox, D. G., on Behalf of the NCI Breast and Prostate Cancer Cohort Consortium, Gaudet, M. M., Stevens, V. L., et al. (2014) · Free Radical Research · doi:10.3109/10715762.2013.875168 · Cited by 40
He, S.-C., Wu, S., Wang, C., Du, X.-D., Yin, G., Jia, Q., Zhang, Y., Wang, L., Soares, J. C., & Zhang, X. Y. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.04.089 · Cited by 28
Kawamura, Y., Liu, X., Shimada, T., Otowa, T., Kakiuchi, C., Akiyama, T., Umekage, T., & Sasaki, T. (2011) · Asia-Pacific Psychiatry · doi:10.1111/j.1758-5872.2011.00132.x · Cited by 7
Metadata
- Authors
- Gauderman, W. J.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2001-12-21
- Volume
- 21
- Issue
- 1
- Pages
- 35-50
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2103093164
- Cited by
- 643
- References
- 0
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
