On the use and misuse of scalar scores of confounders in design and analysis of observational studies

Statistics in Medicine

Volumn 34, Issue 18, 2015, Pages 2618-2635

  Pfeiffer, R M ^a   Riedl, R ^b  

^a NATIONAL CANCER INSTITUTE   (United States)

^b MEDICAL UNIVERSITY OF GRAZ   (Austria)

Author keywords

Balancing scores; Confounder scores; Matched case control study; Misspecified models; Summary scores; Treatment effect

Indexed keywords

AGED; ARTICLE; ASYMPTOTIC BIAS; CASE CONTROL STUDY; COHORT ANALYSIS; CONDITIONAL LOGISTIC REGRESSION; CONFOUNDING VARIABLE; CONTROLLED STUDY; DISEASE RISK SCORE; EXPOSURE EFFECT; FEMALE; HUMAN; LINEAR REGRESSION ANALYSIS; LOGISTIC REGRESSION ANALYSIS; MALE; MATHEMATICAL ANALYSIS; NONLINEAR SYSTEM; NULL HYPOTHESIS; OBSERVATIONAL STUDY; POISSON DISTRIBUTION; PROPENSITY SCORE; SCORING SYSTEM; SIMULATION; STATISTICAL BIAS; STATISTICAL MODEL; STATISTICAL PARAMETERS; UNCONDITIONAL LOGISTIC REGRESSION; VERY ELDERLY; COMPUTER SIMULATION; EPIDEMIOLOGY; METHODOLOGY; PROCEDURES; REGRESSION ANALYSIS;

BIAS (EPIDEMIOLOGY); CASE-CONTROL STUDIES; COHORT STUDIES; COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); EPIDEMIOLOGIC METHODS; HUMANS; LOGISTIC MODELS; OBSERVATIONAL STUDIES AS TOPIC; REGRESSION ANALYSIS; RESEARCH DESIGN;

EID: 84935041945     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6467     Document Type: Article

References (31)

1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70(1):41-55.
2. Imbens G. Nonparametric estimation of average treatment effects under exogeneity: a review. Review of Economics and Statistics 2004; 86(1):4-29.
3. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine 2004; 23(19):2937-2960.
4. Rosenbaum P, Rubin D. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 1985; 39(1):33-38.
5. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research 2011; 46(3):399-424.
6. Howell MD, Novack V, Grgurich P, Soulliard D, Novack L, Pencina M, Talmor D. Iatrogenic gastric acid suppression and the risk of nosocomial clostridium difficile infection. Archives of Internal Medicine 2010; 170(9):784-790.
7. Alarcon GS, McGwin G, Bertoli AM, Fessler BJ, Calvo-Alen J, Bastian HM, Vila LM, JD Reveille . Effect of hydroxychloroquine on the survival of patients with systemic lupus erythematosus: data from lumina, a multiethnic us cohort (lumina l). Annals of the Rheumatic Diseases 2007; 66(9):1168-1172.
8. El-Serag HB, Johnson ML, Hachem C, Morgana RO. Statins are associated with a reduced risk of hepatocellular carcinoma in a large cohort of patients with diabetes. Gastroenterology 2009; 136(5):1601-1608.
9. Michalia M, Kompoti M, Panagiotakopoulou A, Kallitsi G, Charitidi M, Trikka-Graphakos E, Clouva-Molyvdas P. Impact of red blood cells transfusion on ICU-acquired bloodstream infections: a case-control study. Journal of Critical Care 2012; 27(6):655-661.
10. Etminan M, Samii A, Brophy JM. Statin use and risk of epilepsy: a nested case-control study. Neurology 2010; 75(17):1496-1500.
11. Moden B, Merlo J, Ohlsson H, Rosvall M. Psychotropic drugs and falling accidents among the elderly: a nested case control study in the whole population of Scania, Sweden. Journal of Epidemiology and Community Health 2010; 64(5):440-446.
12. Thillemann TM, Pedersen AB, Mehnert F, Johnsen SP, Søballe K. The risk of revision after primary total hip arthroplasty among statin users: a nationwide population-based nested case-control study. Journal of Bone and Joint Surgery - Series A 2010; 92(5):1063-1072.
13. Austin P, Grootendorst P, Normand SL, Anderson G. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Statistics in Medicine 2007; 26(4):754-768.
14. Gail M, Wieand S, Piantadosi S. Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika 1984; 71(3):431-444.
15. Freedman DA. Randomization does not justify logistic regression. Statistical Science 2008; 23(2):237-249.
16. Månsson R, Joffe M, Sun W, Hennessy S . On the estimation and use of propensity scores in case-control and case-cohort studies. American Journal of Epidemiology 2007; 166(3):332-339.
17. Glynn RJ, Gagne JJ, Schneeweiss S. Role of disease risk scores in comparative effectiveness research with emerging therapies. Pharmacoepidemiology and Drug Safety 2012; 21(SUPPL.2):138-147.
18. Miettinen OS. Stratification by a multivariate confounder score. American Journal of Epidemiology 1976; 104(6):609-620.
19. Hansen B. The prognostic analogue of the propensity score. Biometrika 2008; 95(2):481-488.
20. Arbogast PG, Ray WA. Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders. American Journal of Epidemiology 2011; 174(5):613-620.
21. McCullagh P, Nelder J. Generalized Linear Models. Monographs on Statistics & Applied Probability. Chapman & Hall CRC, London, 1989.
22. White H. Maximum likelihood estimation of misspecified models. Econometrica 1982; 50(1):1-25.
23. Leacy F, Stuart E. On the joint use of propensity and prognostic scores in estimation of the average treatment effect on the treated: a simulation study. Statistics in Medicine 2014; 33(20):3488-3508.
24. Gong G, Samaniego F. Pseudo maximum likelihood estimation: theory and applications. Annals of Statistics 1981; 9(4):861-869.
25. Andersen PK, Gill RD. Cox's regression model for counting processes: a large sample study. Annals of Statistics 1982; 10(4):1100-1120.
26. van der Vaart AW 2000; Asymptotic Statistics. Cambridge University Press: Cambridge, United Kingdom.
27. Riedl R, Engels E, Warren J, Berghold A, Ricker W, Pfeiffer R. Blood transfusions and the subsequent risk of cancers in the United States elderly. Transfusion 2013; 53(10):2198-2206.
28. Sekhon JS. Multivariate and propensity score matching software with automated balance optimization: the matching package for R. Journal of Statistical Software 2011; 42(7):1-52.
29. Hade E, Lu B. Bias associated with using the estimated propensity score as a regression covariate. Statistics in Medicine 2014; 33(1):74-87.
30. Hill J. Discussion of research using propensity-score matching: comments on 'A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003' by Peter Austin. Statistics in Medicine 2008; 27(12):2055-2061.
31. Allen AS, Satten GA. Control for confounding in case-control studies using the stratification score, a retrospective balancing score. American Journal of Epidemiology 2011; 173(7):752-760.