Published Nov 2009
Johnson ML, Crown W, Martin BC, et al. Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—part III. Value Health. 2009;12(8):1062-1073.
Objectives: Most contemporary epidemiologic studies require complex
analytical methods to adjust for bias and confounding. New methods are
constantly being developed, and older more established methods are yet
appropriate. Careful application of statistical analysis techniques can
improve causal inference of comparative treatment effects from nonrandomized
studies using secondary databases. A Task Force was formed to
offer a review of the more recent developments in statistical control of
Methods: The Task Force was commissioned and a chair was selected by
the ISPOR Board of Directors in October 2007. This Report, the third in
this issue of the journal, addressed methods to improve causal inference of
treatment effects for nonrandomized studies.
Results: The Task Force Report recommends general analytic techniques
and specific best practices where consensus is reached including: use of
stratification analysis before multivariable modeling, multivariable regression
including model performance and diagnostic testing, propensity
scoring, instrumental variable, and structural modeling techniques including
marginal structural models, where appropriate for secondary data.
Sensitivity analyses and discussion of extent of residual confounding are
Conclusions: Valid findings of causal therapeutic benefits can be produced
from nonrandomized studies using an array of state-of-theart
analytic techniques. Improving the quality and uniformity of these
studies will improve the value to patients, physicians, and policymakers
Keywords: causal inference, comparative effectiveness, nonrandomized studies, research methods, secondary databases.