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Design and Analysis of Clinical Trials.....Overviews and Meta-analysis.. Lesson 16




Lesson 16

 Overviews and Meta-analysis

Introduction

An overview (also called a systematic review) attempts to summarize the scientific evidence related to treatment, causation, diagnosis, or prognosis of a specific disease. An overview does not generate any new data - it reviews and summarizes already-existing studies.
Overviews, which are relied upon by many physicians, are important because there usually exist multiple studies that have addressed a specific research question. Yet these types of studies may differ with respect to:
  • Design
  • Patient population
  • Quality
  • Results
Although it appears that conducting an overview is easy, it requires a good deal of effort and care to do it well. For example, determining inclusion and exclusion criteria for studies is a major challenge for researchers when putting together a useful overview.
What does this process involve? There are six basic steps to an overview:
  1. Define a focused clinical question
  2. Conduct a thorough literature search
  3. Apply inclusion/exclusion criteria to the identified studies
  4. Abstract/summarize the data from the eligible studies
  5. Perform statistical analysis (meta-analysis), if appropriate
  6. Disseminate the results

Learning objectives & outcomes

Upon completion of this lesson, you should be able to do the following:
  1. Describe the processes for conducting a systematic overview.
  2. Describe how publication bias can affect the results of a systematic review.
  3. Recognize patterns in a ‘funnel plot’ that would indicate potential publication bias.
  4. Evaluate the quality of a clinical report with the Jaded scale.
  5. Recognize the appropriate use of a fixed effects model vs. a random effects model for a meta-analysis. State how the weights differ between the fixed and random approaches.
  6. Describe the rationale for a test of heterogeneity among the studies used in a meta-analysis.
  7. Describe methods for performing a sensitivity analysis of the meta-analysis.


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