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Disclaimer: About 80% to 90% of this piece was written by Laurel A. Rockefeller. The other 10% to 20% was written by Arpi Haroutunian.

Laurel A. Rockefeller

Issue 2, September 17, 2004: Risk Supplement Program

Publication
Applying an evidence-based approach to the management of patients with ocular hypertension: evaluating and synthesizing published evidence. Anne L Coleman, MD, PhD; Kuldev Singh, MD, MPH; Richard Wilson, MD et al. Am J Ophthalmol. 2004;138 Supplement: S3-S10.

Discussion synopsis

• The use of evidence-based medicine is surprisingly not universal in medical practices with estimates of opinion-based treatments as high as 35-50% for all treatments. To encourage more universal use of evidence-based medical data and more clinically and statistically significant data, Coleman et al describe the pros and cons of different study designs and methods for interpreting data with special attention to the ophthalmology context and more specifically the treatment of glaucoma.

  -- Coleman et al caution that statistical significance (as seen in P values) and clinical significance are not always one and the same and not to overly rely on P values, but rather to more critically evaluate contextually how the statistically significant effect impacts the patient.

  -- The use of evidence-based medicine (EBM) in the management of patients with OH has been suboptimal because of the limited availability of high quality data from well controlled clinical trials and the lack of formal clinical training in evaluating EBM.

  -- Coleman et al strongly encourage readers to carefully weigh not only clinical and statistical significance, but also the total costs of treatment to patients, including and especially adverse events and side effects.

  • Many valuable insights about use and misuse of study designs and data are demonstrated through analysis of several important studies: EMGT, OHTS, and the Collaborative Normal-Tension Glaucoma Study (CNTGS). This analysis helps the reader better understand these studies specifically as well as arming the reader with a new arsenal of critical tools for assessing the value of medical research as a whole. It helps readers develop strategies and techniques for the critical evaluation of the quality of published data and synthesis, integration of the published evidence into patient care.
    

  • Three major questions are critical in evaluating the quality of evidence and how to integrate the evidence into clinical practice:
    - Are the results of the study valid?
    - What are the study results?
    - Do the study outcomes apply to individual patient care?

  • The paper reviews the concepts of number needed to treat (NNT) and number needed to harm (NNH) as a method of comparing the potential benefit and harm of a given therapy

  • The authors conclude that as the volume of high quality data increases, the information provided in their paper might help ophthalmologists apply the techniques of EBM in their efforts to optimize individual patient care in those with OH.

Strengths

• Demonstrates how different study and data analysis methods work and common ways data is used and misused.

-- P values are great for showing statistical significance (if P < 0.05%, x effect is significant), however a statistically significant cause or effect is not the same as a clinically significant cause or effect since the effect or cause may be meaningless when applied to a specific patient or specific condition.

--Sample sizes can be used to exaggerate results either through small samples and use of percentages (10% is both 1 in 10 and 10 in 100) or large samples and statistical significance (if numbers are large enough, a statistically significant effect will appear, even when random chance is at work).

• Ophthalmic and glaucoma-specific data and examples are used to make points about evidence-based medicine. Especially helpful is the use of OHTS to demonstrate sample size issues along with CNTGS and EMGT to demonstrate Number Needed to Treat (NNT) analyses.

Conclusion
This article effectively describes evidence-based appraisal of medical claims, by demonstrating the strengths and weaknesses of various types of studies and common forms of data analyses.

BACK TO HOMEPAGE

Disclaimer: About 80% to 90% of this piece was written by Laurel A. Rockefeller. The other 10% to 20% was written by Arpi Haroutunian.