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Semiparametric tests for sufficient cause interaction

Stijn Vansteelandt UGent, Tyler J VanderWeele and James M Robins (2012) JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY. 74(2). p.223-244
abstract
A sufficient cause interaction between two exposures signals the presence of individuals for whom the outcome would occur only under certain values of the two exposures. When the outcome is dichotomous and all exposures are categorical, then, under certain no confounding assumptions, empirical conditions for sufficient cause interactions can be constructed on the basis of the sign of linear contrasts of conditional outcome probabilities between differently exposed subgroups, given confounders. It is argued that logistic regression models are unsatisfactory for evaluating such contrasts, and that Bernoulli regression models with linear link are prone to misspecification. We therefore develop semiparametric tests for sufficient cause interactions under models which postulate probability contrasts in terms of a finite dimensional parameter, but which are otherwise unspecified. Estimation is often not feasible in these models because it would require non-parametric estimation of auxiliary conditional expectations given high dimensional variables. We therefore develop multiply robust tests under a union model which assumes that at least one of several working submodels holds. In the special case of a randomized experiment or a family-based genetic study in which the joint exposure distribution is known by design or Mendelian inheritance, the procedure leads to asymptotically distribution-free tests of the null hypothesis of no sufficient cause interaction.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
IDENTIFICATION, ASSOCIATION, MODELS, INFERENCE, Gene-environment interaction, GENE-ENVIRONMENT INTERACTIONS, Synergism, Sufficient cause, INCOMPLETE DATA, EPISTASIS, STATISTICAL INTERACTIONS, Gene-gene interaction, Semiparametric inference, Effect modification, Double robustness, EFFICIENCY, ROBUSTNESS
journal title
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
J. R. Stat. Soc. Ser. B-Stat. Methodol.
volume
74
issue
2
pages
223 - 244
Web of Science type
Article
Web of Science id
000301286200003
JCR category
STATISTICS & PROBABILITY
JCR impact factor
4.81 (2012)
JCR rank
2/117 (2012)
JCR quartile
1 (2012)
ISSN
1369-7412
DOI
10.1111/j.1467-9868.2011.01011.x
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2122740
handle
http://hdl.handle.net/1854/LU-2122740
date created
2012-05-30 16:58:17
date last changed
2013-03-26 14:20:27
@article{2122740,
  abstract     = {A sufficient cause interaction between two exposures signals the presence of individuals for whom the outcome would occur only under certain values of the two exposures. When the outcome is dichotomous and all exposures are categorical, then, under certain no confounding assumptions, empirical conditions for sufficient cause interactions can be constructed on the basis of the sign of linear contrasts of conditional outcome probabilities between differently exposed subgroups, given confounders. It is argued that logistic regression models are unsatisfactory for evaluating such contrasts, and that Bernoulli regression models with linear link are prone to misspecification. We therefore develop semiparametric tests for sufficient cause interactions under models which postulate probability contrasts in terms of a finite dimensional parameter, but which are otherwise unspecified. Estimation is often not feasible in these models because it would require non-parametric estimation of auxiliary conditional expectations given high dimensional variables. We therefore develop multiply robust tests under a union model which assumes that at least one of several working submodels holds. In the special case of a randomized experiment or a family-based genetic study in which the joint exposure distribution is known by design or Mendelian inheritance, the procedure leads to asymptotically distribution-free tests of the null hypothesis of no sufficient cause interaction.},
  author       = {Vansteelandt, Stijn and VanderWeele, Tyler J and Robins, James M},
  issn         = {1369-7412},
  journal      = {JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY},
  keyword      = {IDENTIFICATION,ASSOCIATION,MODELS,INFERENCE,Gene-environment interaction,GENE-ENVIRONMENT INTERACTIONS,Synergism,Sufficient cause,INCOMPLETE DATA,EPISTASIS,STATISTICAL INTERACTIONS,Gene-gene interaction,Semiparametric inference,Effect modification,Double robustness,EFFICIENCY,ROBUSTNESS},
  language     = {eng},
  number       = {2},
  pages        = {223--244},
  title        = {Semiparametric tests for sufficient cause interaction},
  url          = {http://dx.doi.org/10.1111/j.1467-9868.2011.01011.x},
  volume       = {74},
  year         = {2012},
}

Chicago
Vansteelandt, Stijn, Tyler J VanderWeele, and James M Robins. 2012. “Semiparametric Tests for Sufficient Cause Interaction.” Journal of the Royal Statistical Society Series B-statistical Methodology 74 (2): 223–244.
APA
Vansteelandt, S., VanderWeele, T. J., & Robins, J. M. (2012). Semiparametric tests for sufficient cause interaction. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 74(2), 223–244.
Vancouver
1.
Vansteelandt S, VanderWeele TJ, Robins JM. Semiparametric tests for sufficient cause interaction. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY. 2012;74(2):223–44.
MLA
Vansteelandt, Stijn, Tyler J VanderWeele, and James M Robins. “Semiparametric Tests for Sufficient Cause Interaction.” JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 74.2 (2012): 223–244. Print.