Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs

Journal of Internal Medicine

Volumn 275, Issue 6, 2014, Pages 570-580

  Sturmer T ^a   Wyss, R ^a   Glynn, R J ^b   Brookhart, M A ^a  

^a IL   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Comparative effectiveness research; Confounding; Epidemiologic methods; Heterogeneity; Pharmacoepidemiology; Propensity scores

Indexed keywords

ATTRIBUTABLE RISK; CLINICAL PRACTICE; COMORBIDITY; COMPARATIVE EFFECTIVENESS; CONFOUNDING VARIABLE; DISEASE SEVERITY; EPIDEMIOLOGY; HUMAN; INSTRUMENTAL VARIABLE ANALYSIS; LOGISTIC REGRESSION ANALYSIS; MEDICAL DECISION MAKING; OBSERVATIONAL STUDY; PHARMACOEPIDEMIOLOGY; PRACTICE GUIDELINE; PRIORITY JOURNAL; PROBABILITY; PROPENSITY SCORE; REVIEW; STANDARDIZATION; THERAPY EFFECT;

COMPARATIVE EFFECTIVENESS RESEARCH; CONFOUNDING; EPIDEMIOLOGIC METHODS; HETEROGENEITY; PHARMACOEPIDEMIOLOGY; PROPENSITY SCORES;

AGE FACTORS; COMORBIDITY; COMPARATIVE EFFECTIVENESS RESEARCH; CONFOUNDING FACTORS (EPIDEMIOLOGY); DRUG THERAPY, COMBINATION; EPIDEMIOLOGIC RESEARCH DESIGN; HUMANS; OUTCOME ASSESSMENT (HEALTH CARE); PHARMACOEPIDEMIOLOGY; PROPENSITY SCORE; SEX FACTORS;

EID: 84902076643     PISSN: 09546820     EISSN: 13652796     Source Type: Journal    
DOI: 10.1111/joim.12197     Document Type: Review

References (53)

1. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 2000; 342: 1887-92.
2. Miettinen OS. The need for randomization in the study of intended drug effects. Stat Med 1983; 2: 267-71.
3. Yusuf A, Collins R, Peto R. Why do we need some large, simple randomized trials? Stat Med 1984; 3: 409-20.
4. Prentice RL, Langer R, Stefanick ML et al. Combined postmenopausal hormone therapy and cardiovascular disease: toward resolving the discrepancy between observational studies and the women's health initiative clinical trial. Am J Epidemiol 2005; 162: 404-14.
5. Walker AM. Confounding by indication. Epidemiology 1996; 7: 335-6.
6. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41-55.
7. 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: 437-47.
8. Stürmer T, Schneeweiss S, Brookhart MA, Rothman KJ, Avorn J, Glynn RJ. Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs (NSAID) and short-term mortality in the elderly. Am J Epidemiol 2005; 161: 891-8.
9. Rubin DB. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med 2007; 26: 20-36.
10. Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol 2003; 158: 915-20.
11. Glynn RJ, Schneeweiss S, Stürmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 2006; 98: 253-9.
12. Messer LC, Oakes JM, Mason S. Effects of socioeconomic and racial residential segregation on preterm birth: a cautionary tale of structural confounding. Am J Epidemiol 2010; 171: 664-73.
13. Westreich D, Cole SR. Invited commentary: positivity in practice [Commentary]. Am J Epidemiol 2010; 171: 674-7.
14. 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;. doi:10.1093/aje/kwq198
15. Hernán MA, Alonso A, Logan R et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology 2008; 19: 766-79.
16. Brookhart MA, Stürmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding control in healthcare database research: challenges and potential approaches. Med Care 2010; 48(Suppl 1): S114-20.
17. Sackett DL, Richardson WS, Rosenberg W, Haynes RB. Evidence-based Medicine: How to Practice and Teach EBM. New York: Churchill Livingstone, 1997.
18. Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases. N Engl J Med 1998; 338: 1516-20.
19. Glynn RJ, Knight EL, Levin R, Avorn J. Paradoxical relations of drug treatment with mortality in older persons. Epidemiology 2001; 12: 682-9.
20. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology, 3rd edn. Philadelphia: Lippincott Williams & Wilkins, 2008.
21. André T, Boni C, Mounedji-Boudiaf L et al. Oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment for colon cancer. N Engl J Med 2004; 350: 2343-51.
22. Sanoff HK, Carpenter WR, Stürmer T et al. the effect of adjuvant chemotherapy on survival of patients with stage III colon cancer diagnosed after age 75. J Clin Oncol 2012; 30: 2624-34.
23. Steering Committee of the Physicians' Health Study Research Group. Final report on the aspirin component of the ongoing physicians' health study. N Engl J Med 1989; 321: 129-35.
24. Ridker PM, Cook NR, Lee IM et al. A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 2005; 352: 1293-304.
25. Pearl J. Causality: Models, Reasoning, and Inference. New York, NY: Cambridge University Press, 2000.
26. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11: 550-60.
27. Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000; 11: 561-70.
28. Stürmer T, Rothman KJ, Glynn RJ. Insights into different results from different causal contrasts in the presence of effect-measure modification. Pharmacoepidemiol Drug Saf 2006; 15: 698-709.
29. Setoguchi S, Schneeweiss S, Brookhart M, Glynn R, Cook E. Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiol Drug Saf 2008; 17: 546-55.
30. Lee B, Lessler J, Stuart E. Improving propensity score weighting using machine learning. Stat Med 2010; 29: 337-46.
31. Ellis AR, Dusetzina SB, Hansen RA, Gaynes BN, Farley JF, Stürmer T. Propensity scores from logistic regression yielded better covariate balance than those from a boosted model: a demonstration Using STAR*D Data. Pharmacoepidemiol Drug Saf 2011; 20: S129.
32. Imai K, Ratkovic M. Covariate balancing propensity score. 2012; Working paper available at http://imai.princeton.edu/research/CBPS.html.
33. Lunt M, Solomon DH, Rothman KJ et al. Different methods of balancing covariates leading to different effect estimates in the presence of effect modification. Am J Epidemiol 2009; 169: 909-17.
34. Robins JM. Data, design, and background knowledge in etiologic inference. Epidemiology 2001; 12: 313-20.
35. Westreich D, Cole SR, Jonsson Funk M, Brookhart MA, Stürmer T. The role of the c-statistic in variable selection for propensity score models. Pharmacoepidemiol Drug Saf 2011; 20: 317-20.
36. Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T. Variable selection in propensity score models: some insights from a simulation study. Am J Epidemiol 2006; 163: 1149-56.
37. 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: 1213-22.
38. Patrick AR, Schneeweiss S, Brookhart MA et al. The implications of propensity score variable selection strategies in pharmacoepidemiology - an empirical illustration. Pharmacoepidemiol Drug Saf 2011; 20: 551-9.
39. 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: 512-22.
40. Rassen 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: 1404-13.
41. Toh 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: 849-57.
42. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959; 22: 719-48.
43. Stürmer T, Poole C. Matching in cohort studies: return of a long lost family member. Am J Epidemiol 2009;169(Suppl):S128. Symposium. 42nd annual meeting of the Society for Epidemiologic Research, Anaheim, 2009
44. Peters CC. A method of matching groups for experiment with no loss of population. J Educ Research 1941; 34: 606-12.
45. Belson WA. Matching and prediction on the principle of biological classification. J R Stat Soc Ser C Appl Stat 1959; 8: 65-75.
46. Ellis AR, Dusetzina SB, Hansen RA, Gaynes BN, Farley JF, Stürmer T. Investigating differences in treatment effect estimates between propensity score matching and weighting: a demonstration using STAR*D trial data. Pharmacoepidemiol Drug Saf 2013; 22: 138-44.
47. Austin PC. Some methods of propensity-score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations. Biom J 2009; 51: 171-84.
48. Parsons LS. Reducing bias in a propensity score matched-pair sample using greedy matching techniques, 2001. (http://www2.sas.com/proceedings/sugi26/p214-26.pdf).
49. Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology 2003; 14: 680-6.
50. Kurth T, Walker AM, Glynn RJ et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol 2006; 163: 262-70.
51. Hernán MA. A definition of causal effect for epidemiological research. J Epidemiol Community Health 2004; 58: 265-71.
52. Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol 2008; 168: 656-64.
53. Jackson LA, Jackson ML, Nelson JC, Neuzil KM, Weiss NS. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol 2006; 35: 337-44.