Principal interactions analysis for repeated measures data: application to gene-gene and gene-environment interactions-Reference-Cited by-同舟云学术

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Principal interactions analysis for repeated measures data: application to gene-gene and gene-environment interactions

Published:2012-03-13 Issue:22 Volume:31 Page:2531-2551
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statist. Med.

Author:

Mukherjee Bhramar¹,Ko Yi-An¹,VanderWeele Tyler²,Roy Anindya³,Park Sung Kyun⁴,Chen Jinbo⁵

Affiliation:

1. Department of Biostatistics; University of Michigan; Ann Arbor MI 48109 U.S.A.

2. Department of Epidemiology and Biostatistics; Harvard University; Boston MA 02115 U.S.A.

3. Department of Mathematics and Statistics; UMBC; Baltimore MD 21250 U.S.A.

4. Department of Epidemiology; University of Michigan; Ann Arbor MI 48109 U.S.A.

5. Department of Biostatistics; University of Pennsylvania; Philadelphia PA 19104 U.S.A.

Funder

NSF

NIH

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/sim.5315/fullpdf

Reference41 articles.

1. Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies-challenges and opportunities;Khoury;American Journal of Epidemiology,2009

2. Inference in generalized additive mixed models by using smoothing splines;Lin;Journal of the Royal Statistical Society: Series B (Statistical Methodology),1999

3. One degree of freedom for non-additivity;Tukey;Biometrics,1949

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2. Exposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction;Statistics in Medicine;2017-05-11

3. A combination test for detection of gene-environment interaction in cohort studies;Genetic Epidemiology;2017-03-31

4. A comparative study of different covariance structure models for the analysis of repeated measurement data;International Journal of Biomathematics;2016-11-15

5. Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction;Epidemiology;2016-11

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