Studies with many covariates and few outcomes: Selecting covariates and implementing propensity-score-based confounding adjustments

Epidemiology

Volumn 25, Issue 2, 2014, Pages 268-278

  Patorno, Elisabetta ^a   Glynn, Robert J ^a   Hernández Díaz, Sonia ^b   Liu, Jun ^a   Schneeweiss, Sebastian ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ALGORITHMS; ANTICONVULSANTS; CARDIOVASCULAR DISEASES; CONFOUNDING FACTORS (EPIDEMIOLOGY); EPIDEMIOLOGIC RESEARCH DESIGN; FEMALE; FOLLOW-UP STUDIES; HUMANS; MALE; MIDDLE AGED; OUTCOME ASSESSMENT (HEALTH CARE); PROPENSITY SCORE; RISK FACTORS;

EID: 84894153340     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0000000000000069     Document Type: Article

References (42)

1. R obins J. A new approach to causal inference in mortality studies with a sustained exposure period-Application to control of the healthy worker survivor effect. Math Model. 1986;7:1393-1512
2. Van Der Laan MJ, Rose S. Targeted Learning: Causal Inference for Observational and Experimental Data. New York, N.Y.: Springer; 2011
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. Steyerberg EW, Schemper M, Harrell FE. Logistic regression modeling and the number of events per variable: Selection bias dominates. J Clin Epidemiol. 2011;64:1464-1463
5. G lynn RJ, Gagne JJ, Schneeweiss S. Role of disease risk scores in comparative effectiveness research with emerging therapies. Pharmacoepidemiol Drug Saf. 2012;21(suppl 2):138-147
6. R osenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41-55
7. Braitman LE, Rosenbaum PR. Rare outcomes, common treatments: Analytic strategies using propensity scores. Ann Intern Med. 2002;137:693-695
8. DaAgostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265-2281
9. R obins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550-560
10. Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61:962-973
11. L endle SD, Fireman B, Van Der Laan MJ. Targeted maximum likelihood estimation in safety analysis. J Clin Epidemiol. 2013;66(8 suppl):S91-S98
12. T ibshirani R. Regression shrinkage and selection via the Lasso. J Royal Statist Soc B. 1996;58:267-288
13. Setoguchi S, Schneeweiss S, Brookhart MA, Glynn RJ, Cook EF. Evaluating uses of data mining techniques in propensity score estimation: A simulation study. Pharmacoepidemiol Drug Saf. 2008;17:546-555
14. L ee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29:337-346
15. A ustin PC. Using ensemble-based methods for directly estimating causal effects: An investigation of tree-based G-computation. Multivariate Behav Res. 2012;47:115-135
16. R ubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127(8 pt 2):757-763
17. 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
18. 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
19. Brookhart MA, Stürmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding control in healthcare database research: Challenges and potential approaches. Med Care. 2010;48(6 suppl):S114-S120
20. 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
21. R assen JA, Glynn RJ, Brookhart MA, Schneeweiss S. Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. Am J Epidemiol. 2011;173:1404-1413
22. 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
23. C hen H, Deshpande AD, Jiang R, Martin BC. An epidemiological investigation of off-label anticonvulsant drug use in the Georgia Medicaid population. Pharmacoepidemiol Drug Saf. 2005;14:629-638
24. R adley DC, Finkelstein SN, Stafford RS. Off-label prescribing among office-based physicians. Arch Intern Med. 2006;166:1021-1026
25. Bross ID. Spurious effects from an extraneous variable. J Chronic Dis. 1966;19:637-647
26. R assen JA, Doherty M, Huang W, Schneeweiss S. Pharmacoepidemiology Toolbox. Boston, Mass. Available at: Http://www.hdpharmacoepi.org
27. Mintzer S, Skidmore CT, Abidin CJ, et al. Effects of antiepileptic drugs on lipids, homocysteine, and C-reactive protein. Ann Neurol. 2009;65:448-456
28. C alandre EP, Rodriquez-Lopez C, Blazquez A, Cano D. Serum lipids, lipoproteins and apolipoproteins A and B in epileptic patients treated with valproic acid, carbamazepine or phenobarbital. Acta Neurol Scand. 1991;83:250-253
29. N ikolaos T, Stylianos G, Chryssoula N, et al. The effect of long-Term antiepileptic treatment on serum cholesterol (TC, HDL, LDL) and triglyceride levels in adult epileptic patients on monotherapy. Med Sci Monit. 2004;4:MT50-MT52
30. I sojärvi JI, Pakarinen AJ, Myllylä VV. Serum lipid levels during carbamazepine medication. A prospective study. Arch Neurol. 1993;50:590-593
31. I nternational Classification of Diseases, Ninth Revision, Clinical Modification. Washington, D.C.: US Dept. of Health and Human Services; 1988
32. McNamara JO. Pharmacotherapy of the Epilepsies. In, Bruton LL, Lazo JS, Parker KL, eds. Goodman & Gilmanas The Pharmacological Basis of Therapeutics. 11th ed. New York, NY: McGraw-Hill, 2006
33. Perucca E. Clinically relevant drug interactions with antiepileptic drugs. Br J Clin Pharmacol. 2006;61:246-255
34. Solomon DH, Avorn J, Katz JN, et al. Immunosuppressive medications and hospitalization for cardiovascular events in patients with rheumatoid arthritis. Arthritis Rheum. 2006;54:3790-3798
35. R assen JA, Choudhry NK, Avorn J, Schneeweiss S. Cardiovascular outcomes and mortality in patients using clopidogrel with proton pump inhibitors after percutaneous coronary intervention or acute coronary syndrome. Circulation. 2009;120:2322-2329
36. Wahl PM, Rodgers K, Schneeweiss S, et al. Validation of claims-based diagnostic and procedure codes for cardiovascular and gastrointestinal serious adverse events in a commercially-insured population. Pharmacoepidemiol Drug Saf. 2010;19:596-603
37. World Health Organization. International Statistical Classification of Diseases, 10th Revision (ICD-10). Geneva, Switzerland: World Health Organization; 1992
38. Physiciansa Current Procedural Terminology (CPT). 4th ed. Chicago, Ill.: American Medical Association; 1989
39. 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
40. Parsons L. Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques. Proceedings of the 26th Annual SAS User Group International Conference. Cary, N.C.: SAS Institute Inc., 2001. Available at: Http:// www2.sas.com/proceedings/sugi26/p214-26.pdf. Accessed Jun 24, 2013
41. C leveland WS. Robust locally-weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74:829-836
42. T oh S, García Rodríguez LA, Hernán MA. Confounding adjustment via a semi-Automated high-dimensional propensity score algorithm: An application to electronic medical records. Pharmacoepidemiol Drug Saf. 2011;20:849-857