Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases

Epidemiology

Volumn 28, Issue 2, 2017, Pages 237-248

  Schneeweiss, Sebastian ^a   Eddings, Wesley ^a   Glynn, Robert J ^a   Patorno, Elisabetta ^a   Rassen, Jeremy ^b   Franklin, Jessica M ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b Aetion   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PRESCRIPTION DRUG;

ALGORITHM; ARTICLE; COHORT ANALYSIS; ELECTRONIC MEDICAL RECORD; HUMAN; OUTCOME ASSESSMENT; PREDICTION; PRIORITY JOURNAL; PROPENSITY SCORE; BAYES THEOREM; EPIDEMIOLOGY; FACTUAL DATABASE; STATISTICAL MODEL; TREATMENT OUTCOME;

ALGORITHMS; BAYES THEOREM; COHORT STUDIES; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATABASES, FACTUAL; HUMANS; LIKELIHOOD FUNCTIONS; LOGISTIC MODELS; PROPENSITY SCORE; TREATMENT OUTCOME;

EID: 84992363430     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0000000000000581     Document Type: Article

References (40)

1. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press; 2002.
2. G elman A, Carlin J, Stern H, Rubin D. Bayesian Data Analysis. New York, NY: Chapman Hall; 1995.
3. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373-1379.
4. R obins JM, Mark SD, Newey WK. Estimating exposure effects by modelling the expectation of exposure conditional on confounders. Biometrics. 1992;48:479-495.
5. C epeda MS, Boston R, Farrar JT, Strom BL. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am J Epidemiol. 2003;158:280-287.
6. Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T. Variable selection for propensity score models. Am J Epidemiol. 2006;163:1149-1156.
7. Myers JA, Rassen JA, Gagne JJ, et al. Effects of adjusting for instrumental variables on bias and precision of effect estimates. Am J Epidemiol. 2011;174:1213-1222.
8. Brookhart MA, Wang PS, Solomon DH, Schneeweiss S. Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable. Epidemiology. 2006;17:268-275.
9. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20:512-522.
10. Schneeweiss S, Patrick AR, Solomon DH, et al. Comparative safety of antidepressant agents for children and adolescents regarding suicidal acts. Pediatrics. 2010;125:876-888.
11. Patorno E, Bohn RL, Wahl PM, et al. Anticonvulsant medications and the risk of suicide, attempted suicide, or violent death. JAMA. 2010;303:1401-1409.
12. Patorno E, Glynn RJ, Hernández-Díaz S, Liu J, Schneeweiss S. Studies with many covariates and few outcomes: selecting covariates and implementing propensity-score-based confounding adjustments. Epidemiology. 2014;25:268-278.
13. R assen JA, Schneeweiss S. Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system. Pharmacoepidemiol Drug Saf. 2012;21(suppl 1):41-49.
14. Schuster T, Pang M, Platt RW. On the role of marginal confounder prevalence-implications for the high-dimensional propensity score algorithm. Pharmacoepidemiol Drug Saf. 2015;24:1004-1007.
15. Bross ID. Spurious effects from an extraneous variable. J Chronic Dis. 1966;19:637-647.
16. Stürmer T, Rothman KJ, Avorn J, Glynn RJ. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution-a simulation study. Am J Epidemiol. 2010;172:843-854.
17. Tibshirani R. Regression shrinkage and selection via the Lasso J R Stat Soc. 1996;58:267-288.
18. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1-22.
19. G elman A, Jakulin A, Pittau M, Su Y. A weakly informative default prior distribution for logistic and other regression models. Ann Appl Stat. 2008:1360-1383.
20. G elman A, Su Y-S, Yajima M, et al. Data analysis Using regression and Multilevel/Hierarchical Models (r package 'arm'). 2016. URL: https://cran.rproject. org/web/packages/arm/index.html. Accessed October 2016.
21. Friedman J, Hastie T, Simon N, Tibshirani R. Lasso and elastic-net regularized generalized linear models (r package 'glmnet'). 2016. URL: https://cran.r-project.org/web/packages/glmnet/index.html. Accessed October 2016.
22. Fleiss J. Statistical Methods for Rates and Proportions. 2nd ed. New York, NY: Wiley; 1981.
23. Hemant Ishwaran, Kogalur UB, Gorodeski EZ, Minn AJ, Lauer MS. High-dimensional variable selection for survival data. J Am Stat Assoc. 2010;105:205-217.
24. L iaw A, Wiener M. Classification and regression by randomForest. R news. 2002;2:18-22.
25. Breiman l, Cutler A, Liaw A, Wiener M. Breiman and Cutler's random forests for classification and regression (randomForest package in r). 2015. https://cran.r-project.org/web/packages/randomForest/index.html. Accessed October 2016.
26. Breiman L. Random forests. Mach Learn. 2001;45:5-32.
27. Stürmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol. 2006;59:437-447.
28. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, NY: Springer; 2009.
29. Bair E, Tibshirani R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2004;2:E108.
30. C ule E, De Iorio M. Ridge regression in prediction problems: automatic choice of the ridge parameter. Genet Epidemiol 2013;37:704-714.
31. L ockhart R, Taylor J, Tibshirani RJ, Tibshirani R. A significance test for the Lasso. Annals Stat. 2014;42:413-468.
32. Feinstein AR. Clinical biostatistics. XI. Sources of 'chronology bias' in cohort statistics. Clin Pharmacol Ther. 1971;12:864-879.
33. Greenland S. Invited commentary: variable selection versus shrinkage in the control of multiple confounders. Am J Epidemiol. 2008;167:523-529; discussion 530-521.
34. Schafer JL, Kang J. Average causal effects from nonrandomized studies: a practical guide and simulated example. Psychol Methods. 2008;13:279-313.
35. Vansteelandt S, Bekaert M, Claeskens G. On model selection and model misspecification in causal inference. Stat Methods Med Res. 2012;21:7-30.
36. Franklin JM, Schneeweiss S, Polinski JM, Rassen JA. Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases. Comput Stat Data Anal. 2014;72:219-226.
37. Franklin JM, Eddings W, Glynn RJ, Schneeweiss S. Regularized regression versus the high-dimensional propensity score for confounding adjustment in secondary database analyses. Am J Epidemiol. 2015;182:651-659.
38. Zigler CM, Watts K, Yeh RW, Wang Y, Coull BA, Dominici F. Model feedback in Bayesian propensity score estimation. Biometrics. 2013;69:263-273.
39. van der Laan MJ, Gruber S. Collaborative double robust targeted maximum likelihood estimation. Int J Biostat. 2010;6:article 17.
40. De Luna XD, Waernbaum I, Richardson TS. Covariate selection for the nonparametric estimation of an average treatment effect Biometrika. 2011;98:861-875.