• Post category:StudyBullet-7
  • Reading time:3 mins read


An introductory course to understand lucidly the complex science of meta-analysis

What you will learn

Be able to perform meta-analysis

Be able to use the RevMan Software to do meta-analysis

Be able to explain the various types of heterogeneity

Be able to generate and interpret forest and funnel plots

Description

Meta-analysis is an important statistical approach to validly combine the results of studies following a systematic review. This course takes you through the concepts of meta-analysis and the various graphical representation required to display the results. A complete understanding of concepts is required to properly interpret the results to the scientific world. The two important types of data, binary and continuous, and their pooled estimates are discussed in simple language with relevant examples for you to understand better. This will form the basis for your understanding to perform the meta-analysis. The RevMan software offered free by the Cochrane Collaboration is a sturdy application to do meta-analysis, with the image quality good for journal publication. The figures generated are standard and comparable to other images from other applications. I personally will recommend this application for beginners in meta-analysis.

Heterogeneity is variations among the studies which are included in the meta-analysis. Understanding the various types of heterogeneity and how it impacts the interpretation of the results from meta-analysis is important. The course also deals with the learning and generation of forest and funnel plots. They are the graphical presentation of data and results displayed in the form of a scatter plot. These plots give a bird’s eye view of the results, their meaning, and interpretation.


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Come join this course and take yourself to the next level in understanding and interpreting meta-analysis and become a part of cutting-edge research.

English
language

Content

Introduction

Introduction

Data Analysis and Estimates

Lesson 2: Binary Data
Lesson 3: Continous Data
Lesson 4 Pooled estimates in meta-analysis

Heterogeneity and Plots

Lesson 5 Heterogeneity
Lesson 6 Forest plot
Lesson 7 Funnel plot