title: Evidence from nonrandomized studies: a case study on the estimation of causal effects.
abstract: Although randomized controlled trials are regarded as the gold standard for comparison of treatments, evidence from observational studies is still relevant. To cope with the problem of possible confounding in these studies, investigators need methods for analyzing their results which adjust for confounders and lead to unbiased estimation of the treatment effect. In this paper, the authors describe the main principles of three statistical methods for doing this. The first method is the classical approach of a multiple regression model including the effects of treatment and covariates. This considers the relation between prognostic factors and the outcome variable as a relevant criterion for adjustment. The second method is based on the propensity score, focusing on the relation between prognostic factors and treatment assignment. The third method is an ecologic approach using a grouped treatment variable, which may aid in avoiding confounding by indication. These approaches are applied to a partially randomized trial conducted in 720 German breast cancer patients between 1984 and 1997. The study had a comprehensive cohort study design that included recruitment of patients who had consented to participation but not to randomization because of a preference for one of the treatments. This design offers a unique opportunity to contrast results from the nonrandomized portion of a study with those for a randomized subcohort as a reference.
authors.affiliation: Clinical Trials Center, University Medical Center Freiburg, Freiburg, Germany. claudia.schmoor@uniklinik-freiburg.de
authors.forename: Claudia
authors.initials: C
authors.lastname: Schmoor
authors.forename: Angelika
authors.initials: A
authors.lastname: Caputo
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000453:epidemiology* / Q000401:mortality; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003520:Cyclophosphamide / Q000627:therapeutic use; D018572:Disease-Free Survival; D005260:Female; D005472:Fluorouracil / Q000627:therapeutic use; D005858:Germany / Q000453:epidemiology; D006801:Humans; D008727:Methotrexate / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D012044:Regression Analysis; D015983:Selection Bias
chemical_list: D003520:Cyclophosphamide; D005472:Fluorouracil; D008727:Methotrexate