Meta-Analysis for Public Health and Biomedical Research Using R

Ludovic Trinquart, PhD, Assistant Professor, Biostatistics, BUSPH

Michael P. LaValley, PhD, Professor, Biostatistics, BUSPH

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Program Description

Meta-Analysis is the gold standard statistical approach to combine the results of multiple studies and to examine sources of heterogeneity and potential biases. This course will review fixed-effect and random-effects models that underlie the combination of study results in meta-analysis; the use of study-level predictors in meta-regression; assessment of small-study effects and related reporting biases; and sensitivity analyses to bias. Examples will cover meta-analysis of randomized trials and of observational studies. Throughout the course, participants will apply each model by using the R software.

Competencies

Participants will learn to:

  • Utilize the fixed-effect and random-effects methods of combining effect sizes;
  • Describe different ways to measure between-study heterogeneity;
  • Describe the strengths and weaknesses of random-effects as compared to fixed effect meta-analysis;
  • Assess the potential impact of small-study effects and related reporting biases on a combined effect size estimate;
  • Perform meta-regression modeling and describe the limitations of meta-regression

Intended Audience

The target audience includes biostatisticians, data analysts, and quantitative researchers from academia, the pharmaceutical industry, and other government institutions.

Required knowledge/pre-requisites

Basic knowledge of study design and regression modeling, and a basic working knowledge of R are necessary to be successful in the course. Participants must bring a laptop to the class sessions.

Discounts available—visit our FAQs page to learn more.

Low-cost, on-campus housing is also available. Contact us for more information.

Additional Information

Detailed description of sessions

Session 1: Meta-analysis models

Learning objectives:

  • Understand and apply the fixed-effect model
  • Understand and apply DerSimonian and Laird random-effects model
  • Use different estimators of between-trial variance
  • Create forest plots

Session 2: Analyzing non-standard data

Learning objectives:

  • Continuous outcomes
  • Prevalence data or absolute risks
  • Correlated data
  • Observational studies: sensitivity analysis using credibility ceilings

Session 3: Exploring between-trial heterogeneity

Learning objectives:

  • Subgroup analyses
  • Meta-regression analyses

Session 4: Addressing small-study effects and reporting bias

Learning Objectives:

  • Funnel plots
  • Regression-based methods to adjust for small-study effects
  • Copas selection model

Networking reception, interaction with folks in other courses, knowledgeable + honest + experienced faculty.

Summer Institute 2017 Participant

Program Details

On Campus 14 Hours June 14-15, 2018

-Thursday, 9:00am-4:00pm
-Friday, 9:00am-4:00pm