how to extract quantitative data for systematic review and meta-analysis ? – pubrica

An Academic presentation byDr. Nancy Agnes, Head, Technical Operations, Pubrica Group: www.pubrica.comEmail: sales@pubrica.com

HOW TO EXTRACT QUANTITATIVE DATA FOR SYSTEMATIC REVIEW AND META-ANALYSIS

Today's DiscussionOutline

Introduction

Process Involved in the Data Extraction for Meta-

Analysis and Systematic Review

Tools for Data

Extraction

Conclusion

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.

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A meta-analysis often accompanies systematic review.

It involves using statistical methods and techniques to pool the data frommany 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.

Process Involved in the Data Extraction for Meta-Analysis and Systematic ReviewPROCESS 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

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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.

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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 forthe systematic review and Meta-Analysis.

EXAMPLE:

Question: How to extract the data for the management of SuperiorMesenteric Artery Syndrome (SMAS)?

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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.

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After that read sample papers related to SMAS management to identifythe variables.

Freeze template using the variables for SMAS methodology,characteristics, therapeutics,etc.

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DATAEXTRACTION TEMPLATE:

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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 assessthe 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 andmeta- analyses are transparent and complete.

It does not discuss the conductofsystematicreviews explicitly or in-depth,but there are other resources available.

Pubrica develops the PRISMA statement and this explanatory document tohelp authors report a wide array of systematic reviews to assess the benefits and harms.

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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.

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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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