Good Research Practices for Comparative Effectiveness Research:Analytic Methods to Improve Causal Inference from Nonrandomized Studies of Treatment Effects Using Secondary

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 nonran-domized studies using secondary databases. A Task Force was formed to offer a review of the more recent developments in statistical control of confounding. 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 o