Systematic Review and Meta-Analysis

 

Abstract:

Over the past half-century, the scientific literature has grown exponentially. PubMed, only one among more than one-thousand searchable computerized bibliographic data bases, currently includes in excess of 23 million scientific records and adds 500,000 new records annually. Scientific studies have also grown increasingly rigorous. The first randomized controlled trial (RCT) was published in 1948; more than 150,000 RCTs are currently included in the Cochrane Library alone. The rapid growth of rigorous scientific research has also occasioned the development of new methods of research synthesis made possible by the Internet, new indexing and abstracting methods and related advances in statistical methods. Cooper (2010, p.4) noted that the research synthesist summarizes “past research by drawing overall conclusions from many separate investigations that address related or identical hypotheses…and to present the state of knowledge concerning the relation(s) of interest and to highlight important issues that research has left unsolved.”

This two-day workshop is designed for advanced graduate students, faculty, and professional researchers to learn systematic review and meta-analysis methods. The first day will be taught by Professor Howard which will provide students with skills in research synthesis including the ability to identify a suitable research area or issue for the preparation of a systematic review/meta-analysis, employ advanced skills in literature identification and data base searching, collect and code data from identified studies, interpret the evidence collected and present their findings in accordance with best practices reporting standards. Lecture material will be drawn from a diversity of sources, including three Oxford University Press textbooks: “Finding and Evaluating Evidence: Systematic Reviews and Evidence-Based Practice” (Bronson & Davis, 2012); “Systematic Reviews and Meta-Analyses” (Littell, Corcoran, & Pillai, 2008); and “Systematic Syntheses of Qualitative Data” (Saini & Shlonsky, 2012).

The second day will be on meta-analysis using R taught by Professor Din Chen which will be based on his book: “Applied Meta-Analysis Using R (2013)” published by Chapman and Hall/CRC. This tutorial provides a most up-to-date development and a thorough presentation on meta-analysis models with detailed step-by-step illustrations and implementation using R/Stata. The examples are compiled from real applications in public literature and the analyses are illustrated in a step-by-step fashion using the most appropriate R packages and functions which should enable attendees to follow the logic and gain an understanding of the meta-analysis methods and R implementation so that they may use R to analyze their own data. Meta-analysis using “Stata” will be discussed.

 

Outline for Systematic Review (Day 1)

Morning session 1:

• Overview of systematic review issues (definitions, why systematic reviews are needed, differences between systematic and narrative reviews, 7-step systematic review development process)
• Problem formulation, defining the research question, developing a protocol

Morning Session 2:

• Searching the literature, information management issues, using reference data bases, finding the gray literature, using bibliographic reference software
• Gathering information from studies, data extraction methods

Afternoon Session 3:

• Evaluating study quality
• “Best practices” reporting standards for presenting the findings of systematic reviews.

Afternoon Session 4:

• Threats to validity in research syntheses
• Special topics: Stakeholder participation in systematic review development; publication bias; research syntheses and public policy/Cochrane and Campbell Collaborations; journal outlets

 

Outline for Meta-Analysis (Day 2)

Morning session 1:

• Introduction to R
• Overview to meta-analysis for both fixed-effects and random-effects models in meta-analysis. Real datasets in public health are introduced along with two commonly used R packages of “meta” and “rmeta.”

Morning Session 2:

• Meta-analysis models for binary data, such as for risk-ratio, risk difference and odds-ratio
• Meta-analysis models for continuous data, such as for mean difference and standardized mean difference

Afternoon Session 3:

• Methods to quantify heterogeneity and test the significance of heterogeneity among studies in a meta-analysis and then introduce meta-regression with R package of “metaphor.”
• Meta-analysis methods for individual-patient data(IPD) analysis and meta-analysis (MA) with summary statistics

Afternoon Session 4:

• Meta-analysis methods for rare-events which is timely for clinical trials of adverse-events.
• Multivariate meta-analysis and other relevant topics in meta-analysis.

 

Brief Biography:

Professor Matthew Howard received his doctorate from the University of Washington in 1990 and is currently Daniels Distinguished Chair at the School of Social Work, University of North Carolina at Chapel Hill. Dr. Howard is currently editor of the Journal of Addictive Diseases and has published more than 230 peer-reviewed articles, mostly in the mental health and addiction areas. In 2014, Dr. Howard received the University of North Carolina Award for Outstanding Post-Baccalaureate Instruction and he has received many school-levels awards for his teaching excellence. Dr. Howard has also received three grants from the National Institute on Drug Abuse.

Professor Din Chen received his Ph.D. in Statistics from University of Guelph (Canada) in 1995 and he is now the Wallace H. Kuralt Distinguished Professor and Director for the Consortium of Statistical Development and Consultation in Social Intervention Research (http://csdc.web.unc.edu), School of Social Work, University of North Carolina at Chapel Hill. He was a professor in Biostatistics at the University of Rochester Medical Center and the Karl E. Peace endowed eminent scholar chair in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. He has more than 100 referred professional publications and co-authored six books on “Clinical Trial Methodology (2010)”, “Clinical Trial Data Analysis Using R (2010)”, “Interval-Censored Time-to-Event Data: Methods and Applications (2012)”, “Applied Meta-Analysis Using R (2013)”, “Clinical Trial Biostatistics and Biopharmaceutical Applications (2014)” published by Chapman and Hall/CRC, and “Novel Statistical Methods for Public Health Data (2015)” by Springer.

 

May 18, 2016 – May 19, 2016
Tate-Turner-Kuralt Building | Auditorium
325 Pittsboro St.
Chapel Hill, NC 27599-3550
Directions