Copyright © 2021 pubrica. All rights reserved 1

How to Extract Quantitative Data for Systematic

Review and Meta-Analysis

Dr. Nancy Agnes, Head, Technical Operations, Pubrica, sales@pubrica.com

Keywords:

Quantitative Analysis, Quantitative data, Meta-

analysis, Systematic Review, Quantitative Systematic

Review, Survey Methodology, Measuring information,

surgical and conservative management

I. INTRODUCTION

The data extraction is the essential and primary protocol

for conducting a systematic review and meta-analysis.

In order to help, this blog post will introduce you to the

process involved in the data extraction for conducting a

systematic review and meta-analysis. A meta-analysis

often accompanies systematic review. It involves using

statistical methods and techniques to pool the data from

many studies on a particular topic to generate single

quantitative data. This generates an estimate which is

more potent than conventional hypothesis testing. It

generates inferences that are more reliable than reported

by individual studies. A quantitative, formal,

epidemiological study design will be followed for

performing a meta-analysis.

II. PROCESS INVOLVED IN THE DATA

EXTRACTION FOR META-ANALYSIS AND

SYSTEMATIC REVIEW

Process Flow:

Define the review question and establish criteria for

including studies in the review

Search for studies that answer the review question

Choose studies that meet the review criteria

Extract data from included studies

Assess the possibility of bias in included studies by

objectively evaluating them

Evaluate the included data by conducting meta-

analyses where applicable

Address reporting biases

Search strategy:

Various databases are available for the articles

screening for the Systematic Review and Meta-

Analysis, including MEDLINE, PubMed, Scopus and

Cochrane databases. Each database is having a different

search strategy procedure for article screening. The

most commonly used search strategy was Medical

Subject Headings (MeSH) terms (Appendix 2). Below

we have provided the example of the articles screening

process for the systematic review and Meta-Analysis.

Example:

Question: How to extract the data for the management

of Superior Mesenteric Artery Syndrome (SMAS)?

Different combinations of keywords will be used

for the search strategies and medical subject

headings (MeSH) to generate two subsets of

citations: one for SMAS, using the MeSH and

terms like “SMAS”, “SMA syndrome”, “superior

mesenteric artery syndrome” and the other for its

management using terms and MeSH like surgery,

resection, bypass etc.

To search other databases, the keywords were

changed according to each database’s searching

protocol, including management of SMAS

“Superior mesenteric artery syndrome”, “Wilkie

syndrome”, “Cast syndrome”, “mesenteric

duodenal compression syndrome”, “chronic

duodenal ileus”. Bibliographic sources will be

screened.

After that read sample papers related to SMAS

management to identify the variables.

Freeze template using the variables for SMAS

methodology, characteristics, therapeutics,etc.

Copyright © 2021 pubrica. All rights reserved 2

Data Extraction Template:

Risk of bias (quality) assessment)

Quality assessment will be performed as part of the data

extraction process. Two independent reviewers will

critically appraise included studies to assess the risk of

bias using the Quality of Health Economic Studies

(QHES) tool.

Tools For Data Extraction:

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Example of a PRISMA diagram used for data

extraction

PRISMA focuses on how authors can ensure that

systematic reviews and meta-analyses are transparent

and complete. It does not discuss the conduct of

systematic reviews explicitly or in-depth, but there are

other resources available.

Pubrica develops the PRISMA statement and this

explanatory document to help authors report a wide

array of systematic reviews to assess the benefits and

harms. When reporting systematic reviews of non-

randomized trials evaluating the benefits and harms of

interventions, we consider several of the checklist items

significant. We recognize that authors who answer

concerns about aetiology, diagnosis, or prognosis and

who review epidemiological or diagnostic accuracy

research, for example, will need to change or include

additional items in their systematic reviews.

III. CONCLUSION

In future, to obtain the solution for a problem statement,

the hurdles of analytics can be minimized by Data

extraction. Without data extraction, systematic reviews

and meta-analysis proposals threw many human errors

in manipulation and calculations in earlier days. The

evolution of Data extraction scrutinizes and maintaining

the reliability of data in the existing studies.

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