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Introduction to Meta-analysis
Rating: 3.8 out of 5(10 ratings)
27 students

Introduction to Meta-analysis

Statistics, biostatistics, clinical trials, data analysis in medicine.
Created byBioStat Statistics
Last updated 10/2023
English

What you'll learn

  • What aspects to look out for when collecting data for meta-analysis
  • What do we need in order to perform a meta-analysis
  • Which methods can be used in a meta-analysis?
  • What the results of meta-analyses look like and how they should be interpreted

Course content

1 section • 5 lectures • 32m total length

  • Introduction6:32

    Is introductory in nature. It draws attention to the importance and role of meta-analyses in contemporary scientific research.

  • Motivations2:50

    Lesson two explores the motivations of researchers to perform meta-analyses.

  • Theory11:40

    Lesson three addresses theoretical issues and practical aspects related to the interpretation of the results of meta-analyses.

  • Model selection2:18

    Lesson four deals with the selection of the statistical model on the basis of which the meta-analysis will be performed.

  • Performance of meta-analysis9:26

    Lesson five focuses on the practical aspects of performing and interpreting the results of meta-analyses.

Requirements

  • Familiarity with the use of a Windows-based computer program and basis of statistics

Description

The course aims to introduce participant to the basic issues related to meta-analysis. It addresses aspects related to the design of such studies and the interpretation of their results. The author discusses elements such as the forest plot, weighted effect size, heterogeneity coefficient or funnel plot.


What will you learn?

Through this course you will learn a different perspective on statistical analysis. You will learn what to look out for when collecting data for meta-analyses. You will learn about the specific types of models needed to perform meta-analyses. You will learn how a forest plot and a funnel plot are created and how to interpret them, as well as the interpretation of the weighted effect size and the heterogeneity coefficient.


Who this course is designed for:

The course is designed specifically for researchers who want to expand their existing statistical knowledge, in particular:

  • researchers

  • academics

  • PhD students

  • doctors familiar with the basics of statistics

  • managers of pharmaceuticals and clinical research industry.


About us

The course is conducted by Andrzej Tomski, PhD - a lecturer and researcher at the University of Silesia, Faculty of Mathematics. He is a graduate of the Jagiellonian University in Krakow, holding a doctorate in mathematics. He specializes in biomathematics and serves as the person responsible for statistical areas in clinical research (analysis plans, method selection, statistical analysis) at BioStat Research and Development Center.


BioStat Research and Development Center is among the commercial scientific institutions with the status of a Research and Development Center (Centrum Badawczo-Rozwojowe, CBR) registered by the Ministry of Entrepreneurship and Technology.

Who this course is for:

  • Researchers
  • Academics
  • PhD students
  • Doctors familiar with the basics of statistics
  • Managers of pharmaceuticals and clinical research industry

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