title: Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report--Part II.
abstract: OBJECTIVE
  The goal of comparative effectiveness analysis is to examine the relationship between two variables, treatment, or exposure and effectiveness or outcome. Unlike data obtained through randomized controlled trials, researchers face greater challenges with causal inference with observational studies. Recognizing these challenges, a task force was formed to develop a guidance document on methodological approaches to addresses these biases.
  
  
  METHODS
  The task force was commissioned and a Chair was selected by the International Society for Pharmacoeconomics and Outcomes Research Board of Directors in October 2007. This report, the second of three reported in this issue of the Journal, discusses the inherent biases when using secondary data sources for comparative effectiveness analysis and provides methodological recommendations to help mitigate these biases.
  
  
  RESULTS
  The task force report provides recommendations and tools for researchers to mitigate threats to validity from bias and confounding in measurement of exposure and outcome. Recommendations on design of study included: the need for data analysis plan with causal diagrams; detailed attention to classification bias in definition of exposure and clinical outcome; careful and appropriate use of restriction; extreme care to identify and control for confounding factors, including time-dependent confounding.
  
  
  CONCLUSIONS
  Design of nonrandomized studies of comparative effectiveness face several daunting issues, including measurement of exposure and outcome challenged by misclassification and confounding. Use of causal diagrams and restriction are two techniques that can improve the theoretical basis for analyzing treatment effects in study populations of more homogeneity, with reduced loss of generalizability.
authors.affiliation: Express Scripts, St. Louis, MO, USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Cox
authors.forename: Bradley C
authors.initials: BC
authors.lastname: Martin
authors.forename: Tjeerd
authors.initials: T
authors.lastname: Van Staa
authors.forename: Edeltraut
authors.initials: E
authors.lastname: Garbe
authors.forename: Uwe
authors.initials: U
authors.lastname: Siebert
authors.forename: Michael L
authors.initials: ML
authors.lastname: Johnson
mesh_terms: D026683:Advisory Committees; D057186:Comparative Effectiveness Research / Q000379:methods* / Q000592:standards; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection / Q000379:methods; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003657:Decision Making; D064420:Drug-Related Side Effects and Adverse Reactions; D017850:Economics, Pharmaceutical; D006291:Health Policy; D006801:Humans; D038622:Internationality; D019370:Observation; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000592:standards; D012189:Retrospective Studies; D015983:Selection Bias; D014481:United States