Propensity score-based sensitivity analysis method for uncontrolled confounding

American Journal of Epidemiology

Volumn 174, Issue 3, 2011, Pages 345-353

  Li, Lingling ^a   Shen, Changyu ^b   Wu, Ann C ^a,c   Li, Xiaochun ^b  

^a HARVARD MEDICAL SCHOOL   (United States)

^b INDIANA UNIVERSITY   (United States)

^c BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

confounding factors (epidemiology); inverse probability weighting; propensity score; sensitivity analysis; sensitivity function; uncontrolled confounding

Indexed keywords

ASTHMA; BIOLOGICAL UPTAKE; CHILD HEALTH; HEALTH SURVEY; HORMONE; INVERSE ANALYSIS; MEDICAL GEOGRAPHY; NUMERICAL MODEL; OBSERVATIONAL METHOD; PROBABILITY; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS; STEROID;

ARTICLE; ASTHMA; EPIDEMIOLOGY; PROPENSITY SCORE; SENSITIVITY ANALYSIS;

ANALYSIS OF VARIANCE; ANTI-ASTHMATIC AGENTS; ASTHMA; CHILD; CONFIDENCE INTERVALS; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; HUMANS; MATHEMATICS; PROBABILITY; PROPENSITY SCORE; SENSITIVITY AND SPECIFICITY; STATISTICS AS TOPIC;

EID: 79960827244     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwr096     Document Type: Article

References (39)

1. Delaney JA, Platt RW, Suissa S. The impact of unmeasured baseline effect modification on estimates from an inverse probability of treatment weighted logistic model. Eur J Epidemiol. 2009;24(7):343-349.
2. Cornfield J, Haenszel W, Hammond EC, et al. Smoking and lung cancer: recent evidence and a discussion of some questions. J Natl Cancer Inst. 1959;22(1):173-203.
3. Rosenbaum P. Observational Studies. New York, NY: Springer-Verlag New York; 2002.
4. McCandless LC, Gustafson P, Levy AR. A sensitivity analysis using information about measured confounders yielded improved uncertainty assessments for unmeasured confounding. J Clin Epidemiol. 2008;61(3):247-255.
5. Schlesselman JJ. Assessing effects of confounding variables. Am J Epidemiol. 1978;108(1):3-8.
6. Greenland S, Neutra R. An analysis of detection bias and proposed corrections in the study of estrogens and endometrial cancer. J Chronic Dis. 1981;34(9-10):433-438. (Pubitemid 11002117)
7. Flanders WD, Khoury MJ. Indirect assessment of confounding: graphic description and limits on effect of adjusting for covariates. Epidemiology. 1990;1(3):239-246. (Pubitemid 20300460)
8. Lin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics. 1998;54(3):948-963. (Pubitemid 28429289)
9. Greenland S. The impact of prior distributions for uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia. J Am Stat Assoc. 2003;98(461):47-54. (Pubitemid 36446640)
10. Steenland K, Greenland S. Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. Am J Epidemiol. 2004;160(4):384-392. (Pubitemid 39045270)
11. Greenland S. Multiple-bias modelling for analysis of observational data. J R Stat Soc Ser A Stat Soc. 2005;168(2): 267-306. (Pubitemid 40427711)
12. MacLehose RF, Kaufman S, Kaufman JS, et al. Bounding causal effects under uncontrolled confounding using counterfactuals. Epidemiology. 2005;16(4):548-555. (Pubitemid 40847841)
13. Stürmer T, Glynn RJ, Rothman KJ, et al. Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information. Med Care. 2007; 45(10 suppl 2):S158-S165.
14. Stürmer T, Schneeweiss S, Rothman KJ, et al. Performance of propensity score calibration-a simulation study. Am J Epidemiol. 2007;165(10):1110-1118. (Pubitemid 47233723)
15. Arah OA, Chiba Y, Greenland S. Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders. Ann Epidemiol. 2008;18(8):637-646. (Pubitemid 352001388)
16. Robins JM, Rotnitzky A, Scharfstein DO. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In: Halloran ME, Berry D, eds. Statistical Models in Epidemiology: The Environment and Clinical Trials. New York, NY: Springer-Verlag New York; 1999:1-92.
17. Brumback BA, Hernán MA, Haneuse SJ, et al. Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures. Stat Med. 2004; 23(5):749-767. (Pubitemid 38279511)
18. 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(5): 561-570. (Pubitemid 30660035)
19. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550-560. (Pubitemid 30660034)
20. Rudin W. Functional Analysis. New York, NY: McGraw-Hill Company; 1973.
21. Rudin W. Principles of Mathematical Analysis. Vol 3. New York, NY: McGraw-Hill Company; 1976.
22. Robins JM. Marginal structural models versus structural nested models as tools for causal inference. In: Halloran E, Barry D, eds. Statistical Models in Epidemiology: The Environment and Clinical Trials. New York, NY: Springer- Verlag New York; 1999:95-134.
23. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Am Stat Assoc. 1952;47(260):663-685.
24. Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994;89(427):846-866.
25. Robins JM, Rotnitzky A, Zhao LP. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J Am Stat Assoc. 1995;90(429):106-121.
26. Robins JM. Association, causation, and marginal structural models. Synthese. 1999;121(1-2):151-179.
27. McCaffrey DF, Ridgeway G, Morral AR. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods. 2004;9(4): 403-425. (Pubitemid 40005040)
28. Tsiatis AA, Davidian M. Comment: demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data. Stat Sci. 2007;22(4):569-573.
29. Efron B, Tibshirani R. An Introduction to the Bootstrap. Boca Raton, FL: CRC Press; 1997.
30. Wu AC, Li L, Miroshnik I, et al. Outcomes after periodic use of inhaled corticosteroids in children. J Asthma. 2009; 46(5):517-522.
31. Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158(9): 915-920. (Pubitemid 37323253)
32. Schneeweiss S, Patrick AR, Solomon DH, et al. Variation in the risk of suicide attempts and completed suicides by antidepressant agent in adults: a propensity score-adjusted analysis of 9 years' data. Arch Gen Psychiatry. 2010;67(5): 497-506.
33. Comprehensive R Archive Network. twang: Toolkit for Weighting and Analysis of Nonequivalent Groups [software]. Vienna, Austria: R Foundation for Statistical Computing; 2009. (http://cran.r-project.org/web/packages/twang/ index.html). (Accessed January 15, 2010).
34. National Heart, Lung, and Blood Institute. Guidelines for the Diagnosis and Management of Asthma (Expert Panel Report 3). Bethesda, MD: National Heart, Lung, and Blood Institute; 2007. (http://www.nhlbi.nih.gov/guidelines/asthma/). (Accessed January 15, 2010).
35. Boushey HA, Sorkness CA, King TS, et al. Daily versus as-needed corticosteroids for mild persistent asthma. National Heart, Lung, and Blood Institute's Asthma Clinical Research Network. N Engl J Med. 2005;352(15):1519- 1528.
36. Papi A, Canonica GW, Maestrelli P, et al. Rescue use of beclomethasone and albuterol in a single inhaler for mild asthma. BEST Study Group. N Engl J Med. 2007;356(20): 2040-2052. (Pubitemid 46789894)
37. Peters SP, Anthonisen N, Castro M, et al. Randomized comparison of strategies for reducing treatment in mild persistent asthma. American Lung Association Asthma Clinical Research Centers. N Engl J Med. 2007;356(20): 2027-2039. (Pubitemid 46789893)
38. Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61(4):962-973.
39. 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(3):262-270. (Pubitemid 43145754)