Overview of the Systematic Review Process Basics of Data Extraction Practice

1. Data Extraction
Dr. Ekpereonne Esu FRSPH
Systematic Review Webinar Series
26th January 2024

2. • Senior Lecturer at the Department of Public
Health, University of Calabar, Calabar, Nigeria
• Member, International Society for Evidence-
Based Healthcare
• Affiliated with Cochrane Nigeria
• No conflicts of interest to declare
• ekpereonneesu@gmail.com

3. Outline
01 04
02 05
03 06
What is data
extraction?
What data do we
collect?
Collecting outcome
data
Handling data in
many formats
Forms and author
support tools
Minimising bias in
data extraction
07 Data extraction in
practice

4. What is data extraction?
01

5. Data extraction…
Involves systematically
collecting
relevant information from
selected studies to
address the research
questions or objectives of
the systematic review
Steps to conducting a systematic review
1. define the question
2. plan eligibility criteria
3. plan methods
4. search for studies
5. apply eligibility criteria
6. collect data
7. assess studies for risk of bias
8. analyse and present results
9. interpret results and draw
conclusions
After you’ve selected the studies to be included in your review,
you need to collect the data from them

6. Why is data extraction important?
● To summarize studies in a common format to facilitate synthesis and
lucid presentation of data
● To identify numerical data (means, counts, rates) for meta-analyses
● To obtain information to assess more objectively the risk of bias in
and applicability of studies
● To identify systematically missing or incorrectly assessed data,
outcomes that are never studied, and underrepresented populations

7. Data extraction
● Extracted data should:
○ Accurately reflect information reported by primary authors
○ Remain in a form close to the original reporting, for easy resolution of
disputes
○ Provide sufficient information to understand the studies and to perform
analyses
● Extract only the data needed
● Different research questions may have different data needs
● Requires familiarity with the content and knowledge of epidemiological
principles and statistical concepts

8. Sources of Data
● Journal articles
● Conference abstracts
● Errata and letters
● Trial registers
● Regulatory reviews
● Individual patient data
● Correspondence with authors

9. What data do we collect?
02

10. What data do we collect?
• You need to collect a wide range of information about each
study:
• everything you will want to report and analyse in your review, and
• everything your readers will want to know about your included
studies

11. What data do we collect?
• You’ll need to collect information on:
• the population
• the setting
• the intervention
• Consider those factors and variations in the population and intervention
that you think might have an impact on the results of the study and that
you specified in your protocol that you would like to investigate.
• The readers of your review will want information detailed enough to help
them decide whether to apply the results in their context

12. What data do we collect?
• The information collected from the studies is required for the
following sections of the systematic review:
• References
• Description of included studies
• Risk of bias assessment
• Analyses (including meta-analysis)
• GRADE assessment

13. Collecting data:
Items to consider
Available from www.training.cochrane.org/handbook

14. Collecting outcome data
03

15. Data in many formats…
• We can choose the most
commonly reported
measure
• We can also do some
conversion of results into
more useful formats
• We can contact study
authors for additional
information.
In some cases, different
studies report data in
different formats, making
it difficult to combine the
studies.
What are our options?

16. Forms and author support tools
05

17. Data collection forms
• Data collection forms must
adapted and tailored to your
review
• The form could be electronic
(Excel spreadsheet) or paper
A data collection form is a crucial
tool to help you organise the
collection of all the information.
• reminds you what to collect
• clarifies what was not
reported in the study
• records the decisions you
make about each study
• source document for data
entry into your review

18. What to include in data collection forms
• source of each piece of
information (e.g. page no.)
• tick boxes or coded options
to save time
• ‘not reported’ and ‘unclear’
options
• review title
• name of author completing
the form
• Study ID (and Record ID if
multiple reports of a study)
• plenty of space for notes
• eligibility criteria at the
beginning

19. Minimising bias in data extraction
06

20. Best practices for data extraction
• two authors should independently collect study characteristics and
outcome data
- reduces error
-checks agreement on subjective judgments and interpretations
• resolving disagreements
- can usually be resolved by discussion
- if not, refer to a third author
• Train reviewers and pilot data collection form
-include each person assisting
-check criteria are consistently applied
-may need to revise the form or instructions
• Contact study authors to obtain unreported data or to clarify unclear data

21. Author support tools

22. Data extraction in practice
07

23. Eligibility criteria
Randomised controlled trials (RCTs) of diet or physical activity
interventions, or combined diet and physical activity interventions, for
preventing overweight or obesity in children (0 17 years) that reported
outcomes at a minimum of 12 weeks from baseline.

24. Population: Children (0-17 years)
Intervention(s):
1. Diet
2. Physical activity
3. Diet + Physical activity
Comparison(s):
• no treatment or
• usual care, or
• another active intervention
Outcomes:
• zBMI score/BMI
•Prevalence of overweight and
obesity
•Weight and height
•Ponderal index
• Percent fat content
•Skin‐fold thickness
Study design: RCT

31. Reporting Data extraction in the protocol
The minimum you should include in your protocol :
• data categories to be collected
• whether two authors will independently extract data
• piloting and use of instructions for data collection form
• how disagreements will be managed
• processes for managing missing data

32. In Summary…
Consider what data
would be necessary
to extract
Ensure you design
and pilot a data
collection form
Data extraction
should be done
independently by
author pairs
Think critically Design and
pilot
Minimise error
& bias

33. You can have data without information, but you cannot
have information without data. Daniel Keys Moran

35. Further guidance
● Li T, Higgins JPT, Deeks JJ (editors). Chapter 5: Collecting data. In: Higgins JPT, Thomas J,
Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic
Reviews of Interventions version 6.1 (updated September 2020). Cochrane, 2020. Available
from www.training.cochrane.org/handbook.