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 confounding.
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.
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 discussed.
Valid findings of causal therapeutic benefits can be produced from nonrandomized studies
using an array of state-of-the-art analytic techniques. Improving the quality and
uniformity of these studies will improve the value to patients, physicians, and policymakers