Evidence-Based Medicine and Comparative Effectiveness Research

Author(s):  
M. Hassan Murad ◽  
Erik P. Hess ◽  
Victor M. Montori
2011 ◽  
Vol 25 (3) ◽  
pp. 191-209 ◽  
Author(s):  
Maria C. Katapodi ◽  
Laurel L. Northouse

The increased demand for evidence-based health care practices calls for comparative effectiveness research (CER), namely the generation and synthesis of research evidence to compare the benefits and harms of alternative methods of care. A significant contribution of CER is the systematic identification and synthesis of available research studies on a specific topic. The purpose of this article is to provide an overview of methodological issues pertaining to systematic reviews and meta-analyses to be used by investigators with the purpose of conducting CER. A systematic review or meta-analysis is guided by a research protocol, which includes (a) the research question, (b) inclusion and exclusion criteria with respect to the target population and studies, © guidelines for obtaining relevant studies, (d) methods for data extraction and coding, (e) methods for data synthesis, and (f ) guidelines for reporting results and assessing for bias. This article presents an algorithm for generating evidence-based knowledge by systematically identifying, retrieving, and synthesizing large bodies of research studies. Recommendations for evaluating the strength of evidence, interpreting findings, and discussing clinical applicability are offered.


2017 ◽  
Vol 19 (10) ◽  
pp. 1081-1091 ◽  
Author(s):  
Kathryn A. Phillips ◽  
Patricia A. Deverka ◽  
Harold C. Sox ◽  
Muin J. Khoury ◽  
Lewis G. Sandy ◽  
...  

2020 ◽  
Vol 48 (5) ◽  
pp. E7
Author(s):  
Robert E. Harbaugh

This review article analyzes the present evidence-based medicine (EBM) algorithm, compares it to the science of practice (SOP) algorithm, and demonstrates how the SOP can evolve from a quality assurance and quality improvement tool into a clinical research tool. Using appropriately constructed prospective observational databases (PODs), the SOP algorithm can be used to draw causal inferences from nonrandomized data, perform innovative comparative effectiveness research, and generate reliable information that can be used to guide treatment decisions.


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