Evidence from nonrandomized studies: A case study on the estimation of causal effects

American Journal of Epidemiology

Volumn 167, Issue 9, 2008, Pages 1120-1129

  Schmoor, Claudia ^a   Caputo, Angelika ^a   Schumacher, Martin ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Breast neoplasms; Causality; Confounding factors (epidemiology); Observation; Regression analysis; Research design; Selection bias

Indexed keywords

CYCLOPHOSPHAMIDE; FLUOROURACIL; METHOTREXATE; TAMOXIFEN;

CANCER; COHORT ANALYSIS; COMPARATIVE STUDY; COVARIANCE ANALYSIS; DISEASE TREATMENT; EPIDEMIOLOGY; MULTIPLE REGRESSION; RESEARCH WORK; TUMOR;

ARTICLE; BREAST CANCER; CANCER COMBINATION CHEMOTHERAPY; CASE STUDY; CAUSAL MODELING; CLINICAL TRIAL; COHORT ANALYSIS; CONFOUNDING VARIABLE; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; COVARIANCE; EVIDENCE BASED MEDICINE; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MULTIPLE CYCLE TREATMENT; MULTIPLE REGRESSION; OUTCOME ASSESSMENT; PROGNOSIS; RANDOMIZATION; RANDOMIZED CONTROLLED TRIAL; SCORING SYSTEM; STATISTICAL ANALYSIS; THERAPY EFFECT;

ANTINEOPLASTIC COMBINED CHEMOTHERAPY PROTOCOLS; BREAST NEOPLASMS; CAUSALITY; CONFOUNDING FACTORS (EPIDEMIOLOGY); CYCLOPHOSPHAMIDE; DISEASE-FREE SURVIVAL; FEMALE; FLUOROURACIL; GERMANY; HUMANS; METHOTREXATE; RANDOMIZED CONTROLLED TRIALS AS TOPIC; REGRESSION ANALYSIS; SELECTION BIAS;

CENTRAL EUROPE; EURASIA; EUROPE; GERMANY;

EID: 42649089469     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwn010     Document Type: Article

References (29)

1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41-55.
2. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol 2000;29:722-9.
3. Johnston SC, Henneman T, McCulloch CE, et al. Modelling treatment effects on binary outcomes with grouped treatment variables and individual covariates. Am J Epidemiol 2002;156: 753-60.
4. Wen SW, Kramer MS. Use of ecological studies in the assessment of intended treatment effects. J Clin Epidemiol 1999;52:7-12.
5. Olschewski M., Scheurlen H. Comprehensive cohort study: an alternative to randomized consent design in a breast preservation trial. Methods Inf Med 1985;24:131-4.
6. Schmoor C, Olschewski M, Schumacher M. Randomized and nonrandomized patients in clinical trials: experiences with comprehensive cohort studies. Stat Med 1996;15: 263-71.
7. D'Agostino RB, D'Agostino RB. Estimating treatment effects using observational data. JAMA 2007;297:314-6.
8. Schumacher M, Bastert G, Bojar H, et al. Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German Breast Cancer Study Group (GBSG) J Clin Oncol 1994;12: 2086-93.
9. Sauerbrei W, Bastert G, Bojar H, et al. Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients: an update based on 10 years follow up. German Breast Cancer Study Group (GBSG). J Clin Oncol 2000;18:94-101.
10. Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control Clin Trials 1996;17:343-6.
11. Cox DR. Regression models and life tables (with discussion). J R Stat Soc Ser B 1972;34:187-220.
12. Kaplan EL, Meier P. Nonparametric estimation from incomplete observation. J Am Stat Assoc 1958;53:457-81.
13. Makuch RW. Adjusted survival curve estimation using covariates. J Chronic Dis 1982;35:437-43.
14. Teräsvirta T, Mellin I. Model selection criteria and model selection tests in regression models. Scand J Stat 1986;13: 159-71.
15. Schneeweis S, Solomon DH, Wang PS, et al. Simultaneous assessment of short-term gastrointestinal benefits and cardiovascular risks of selective cyclooxygenase 2 inhibitors and nonselective nonsteroidal antiinflammatory drugs: an instrumental variable analysis. Arthritis Rheum 2006;54: 3390-8.
16. SAS Institute, Inc. SAS language, version 8. Cary, NC: SAS Institute, Inc, 1999.
17. Brookhart MA, Schneeweiss S, Rothman KJ, et al. Variable selection for propensity score models. Am J Epidemiol 2006;163:1149-56.
18. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med 2007;26:734-53.
19. 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.
20. Stukel TA, Fisher ES, Wennberg DE, et al. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA 2007;297:278-85.
21. Johnston SC. Combining ecological and individual variables to reduce confounding by indication: case study - subarachnoid hemorrhage treatment. J Clin Epidemiol 2000;53:1236-41.
22. Clayton DG, Cuzick J. Multivariate generalizations of the proportional hazards model (with discussion). J R Stat Soc Ser A 1985;148:82-117.
23. Senn S, Graf E, Caputo A. Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure. Stat Med 2007;26: 5529-44.
24. Schumacher M, Olschewski M, Schmoor C. The impact of heterogeneity on the comparison of survival times. Stat Med 1987;6:773-84.
25. Austin PC, Grootendorst P, Normand SLT, et al. Conditioning on the propensity score can result in biased estimation of common measures of treatment effects: a Monte Carlo study. Stat Med 2007;26:754-68.
26. Johnston KM, Gustafson P, Levy AR, et al. Use of instrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research. Stat Med 2007;Sep 10 [Epub ahead of print].
27. Shah B, Laupacis A, Hux J, et al. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol 2005;58:550-9.
28. Stürmer T, Joshi M, Glynn RJ, et al. 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.
29. Stürmer T, Schneeweiss S, Brookhart MA, et al. Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs and short-term mortality in the elderly. Am J Epidemiol 2005;161:891-8.