Recommended: The Empirical Evidence of Bias in Trials Measuring Treatment Differences

When I wrote a book about Evidence Based Medicine back in 2006, I talked about empirical evidence to support the use of certain research methodologies like blinding and allocation concealment. Since that time, many more studies have appeared, more than you or I could easily keep track of. Thankfully, the folks at the Agency for Healthcare Research and Quality commissioned a report to look at studies that empirically evaluate the bias reduction of several popular approaches used in randomized trials. These include

selection bias through randomization (sequence generation and allocation concealment); confounding through design or analysis; performance bias through fidelity to the protocol, avoidance of unintended interventions, patient or caregiver blinding and clinician or provider blinding; detection bias through outcome assessor and data analyst blinding and appropriate statistical methods; detection/performance bias through double blinding; attrition bias through intention-to-treat analysis or other approaches to accounting for dropouts; and reporting bias through complete reporting of all prespecified outcomes.

The general finding was that failure to use these bias reduction approaches tended to exaggerate treatment effects, but the magnitude and precision of these exaggerated effects was inconsistent.

Nancy D Berkman, P Lina Santaguida, Meera Viswanathan, and Sally C Morton. The Empirical Evidence of Bias in Trials Measuring Treatment Differences. Available at