Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses. Franklin, J. M., Eddings, W., Glynn, R. J., & Schneeweiss, S. Am J Epi, 182(7):651-659, Oxford University Press, October, 2015.
doi  abstract   bibtex   
Selection and measurement of confounders is critical for successful adjustment in nonrandomized studies. Although the principles behind confounder selection are now well established, variable selection for confounder adjustment remains a difficult problem in practice, particularly in secondary analyses of databases. We present a simulation study that compares the high-dimensional propensity score algorithm for variable selection with approaches that utilize direct adjustment for all potential confounders via regularized regression, including ridge regression and lasso regression. Simulations were based on 2 previously published pharmacoepidemiologic cohorts and used the plasmode simulation framework to create realistic simulated data sets with thousands of potential confounders. Performance of methods was evaluated with respect to bias and mean squared error of the estimated effects of a binary treatment. Simulation scenarios varied the true underlying outcome model, treatment effect, prevalence of exposure and outcome, and presence of unmeasured confounding. Across scenarios, high-dimensional propensity score approaches generally performed better than regularized regression approaches. However, including the variables selected by lasso regression in a regular propensity score model also performed well and may provide a promising alternative variable selection method. The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
@article{fra15reg,
  title = {Regularized {{Regression Versus}} the {{High}}-{{Dimensional Propensity Score}} for {{Confounding Adjustment}} in {{Secondary Database Analyses}}.},
  volume = {182},
  issn = {1476-6256},
  abstract = {Selection and measurement of confounders is critical for successful adjustment in nonrandomized studies. Although the principles behind confounder selection are now well established, variable selection for confounder adjustment remains a difficult problem in practice, particularly in secondary analyses of databases. We present a simulation study that compares the high-dimensional propensity score algorithm for variable selection with approaches that utilize direct adjustment for all potential confounders via regularized regression, including ridge regression and lasso regression. Simulations were based on 2 previously published pharmacoepidemiologic cohorts and used the plasmode simulation framework to create realistic simulated data sets with thousands of potential confounders. Performance of methods was evaluated with respect to bias and mean squared error of the estimated effects of a binary treatment. Simulation scenarios varied the true underlying outcome model, treatment effect, prevalence of exposure and outcome, and presence of unmeasured confounding. Across scenarios, high-dimensional propensity score approaches generally performed better than regularized regression approaches. However, including the variables selected by lasso regression in a regular propensity score model also performed well and may provide a promising alternative variable selection method.  The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.},
  number = {7},
  journal = {Am J Epi},
  doi = {10.1093/aje/kwv108},
  author = {Franklin, Jessica M. and Eddings, Wesley and Glynn, Robert J. and Schneeweiss, Sebastian},
  month = oct,
  year = {2015},
  keywords = {penalization,simulation-setup,propensity-score},
  pages = {651-659},
  publisher = {Oxford University Press},
  citeulike-article-id = {13898359},
  citeulike-attachment-1 = {fra15reg.pdf; /pdf/user/harrelfe/article/13898359/1048898/fra15reg.pdf; 39307cda8e267a66c86bee376049380256c9fc8c},
  citeulike-linkout-0 = {http://dx.doi.org/10.1093/aje/kwv108},
  citeulike-linkout-1 = {http://aje.oxfordjournals.org/content/early/2015/08/01/aje.kwv108.abstract},
  citeulike-linkout-2 = {http://aje.oxfordjournals.org/content/early/2015/08/01/aje.kwv108.full.pdf},
  citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/26233956},
  citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=26233956},
  day = {1},
  pmid = {26233956},
  posted-at = {2016-01-05 23:01:42},
  priority = {0},
  annote = {plasmode simulation; references Bross method of simultaneously scoring association with exposure, association with outcome, and prevalence}
}
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