Related Experiment Video
Updated: Jun 13, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
Defining comparative effectiveness research: the importance of getting it right
1Dartmouth Medical School, NH, USA. hsox@comcast.net
Abstract:
Defining comparative effectiveness research (CER) was the first order of business for the Institute of Medicine Committee on Initial Priorities for CER. The Institute of Medicine committee approached the task of defining CER by identifying the common theme in the 6 extant definitions. The definition follows: "Comparative effectiveness research is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care. The purpose of CER is to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels." The key words in this definition are "generation and synthesis of evidence" (which implies both original research and systematic reviews), "alternative methods" (which implies making head to head comparisons in study populations typical of daily practice), and "to make informed decisions" (which implies a focus on data that helps to decide between alternatives). Defining CER requires us to decide what we want from decisions about health care. Definitions also serve a bureaucratic function: they can set boundaries that delineate which research is eligible for CER program funding. Definitions--and the funding that advances their goals--can reshape the research environment.
Related Concept Videos
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bias in Epidemiological Studies
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Comparing the Survival Analysis of Two or More Groups
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

