data extraction
DESCRIPTION
Cochrane Review author training workshop, January 22-23, 2009 at the University of Calgary Health Sciences CentreTRANSCRIPT
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Data Extraction
Handbook, Chapter 7
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Data Collection Form
Importance Linked directly to review question and
planned assessment Historical record of decisions through the
review process Data repository, from where the analysis will
emerge Vary across reviews, but fundamental
components
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Components of Data Form
Paper vs electronic forms → Chapter 7.5.2 for considerations
Consider how much information to collect (too much vs too little)
Careful thought and planning
Logical to entry into RevMan, especially for electronic forms
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Components of Data Form (continued)
Items to include:– Review title, author name, who collecting data– Review unique ID, Study ID (RevMan), unique
report ID (multiple reports)– Date (for multiple chronologic versions)– Notes section (up front) – use for RevMan– Verification of study eligibility Table of excluded studies and reasons required– Study characteristics, information to assess bias,
results– Accurate coding: instructions and decision rules,
use of ‘Not reported’ and ‘Unclear’
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Details for Protocol
Data categories to be collected– Again, the study is the unit of interest
Who and how many for collection– 2 people, independent– Content and non-content expert?
Piloting, training, existence of coding instructions for the data form– Piloting for information and instructions– Kappa not routinely done, but can and only for the
most important data
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Details for Protocol (continued)
How data extracted for multiple reports of the same study– How to collate: CONSORT flow diagrams may
help How disagreements handled
– Process (consensus → arbitrator → study authors → report disagreement in review)
Blinding to aspects of study reports not generally recommended
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Data Form: Process
Think – what are the needs? Design – draft form Pilot – sample of papers, compare completed
forms Refine – modify form, instructions Extract – further revisions may be needed
once data extraction underway, this is okay
Consider retraining or recoding with passage of time, also for updating
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Characteristics of Included Studies
Extracted data:– Methods– Participants– Interventions– Comparisons– Outcomes– Information for risk of bias (later)– Notes
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Characteristics of Included Studies
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Characteristics of Included Studies
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Characteristics of Included Studies
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Table of Excluded Studies
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Studies Awaiting Classification
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Table of Ongoing Studies
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Data Types
Dichotomous Continuous Ordinal Counts and rates Time-to-event
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Dichotomous (binary) Outcomes
The outcome is one of two possibilities, cannot be both e.g., pregnant vs. not
Intervention
Event No event
a b
c d
Event No event
Control
a+b = nI
c+d = nC
Data required: ‘event’ and ‘no event’ for
each group
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Dichotomous outcomes (continued)
May experience difficulties with:– Poor reporting– Numbers may need to be derived from
percentage data provided in the report (which denominator to use, compatibility with more than one numerator)
Sometimes ordered categorical data (ordinal) are treated as dichotomous data
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Continuous outcomes
Outcomes that can take any value in a specific range eg, weight, length of stay
Sometimes data from ordered categories (ordinal) are treated as continuous
Check if data can be treated as continuous (Consult CRG statistician)
Effect measures: mean difference (difference in means) or standardized mean difference (factors in standard deviation)
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Ordinal outcomes
Categories with a natural order Can range in number of categories Measurement scales: important to know if
validated Discussion on how to analyze: Section 9.2.4 Analyze as dichotomous? Continuous? As
is? Consult CRG statistician Extract data in all forms in which reported
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Counts of events
Events that can happen more than once to a
given individual Eg, MI, adverse event Common vs. rare events Different methods exist for analysis Consult CRG statistician Extract data in the form they are reported in
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Time-to-event outcomes
Analysis of whether the event occurred and when ‘Survival data’ in statistics E.g., survival, disease recurrence For each individual:
– ‘no event’ period– at the end of that period, whether event occurred
or is just the end of observation (censored) Hazard ratio the most appropriate effect measure Methods of meta-analysis Section 9.4.9; consult
CRG statistician
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Planning Your Analysis
Handbook, Chapter 9
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Planning Your Analysis
Specify comparisons First and most important step! Back to PICO – should relate clearly and directly
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What Comparisons are Important?
Pair-wise comparisons– Glucosamine versus placebo– Glucosamine versus no therapy– Glucosamine versus NSAIDs– Acupuncture plus vitamin B12 injections versus
B12 injections alone
Specify the main comparisons in the protocol If need to modify in light of the data (eg,new
comparison)…document!
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Characteristics of Intervention and Control
Are the interventions or controls all the same?
Different types of drugs used (eg inhaled steroids? NSAIDs?)
Different dosages/duration of therapy/preparation
If not the same, are they similar enough to be combined?
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Population Description
Separate or combine? E.g. Mild versus severe rheumatoid arthritis Age issues? Defining separation points?
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Outcomes
Determine what to combine – shouldn’t be too diverse
Start with the outcomes that are considered most important (specified in protocol)
Eg, mortality, pain, function
May be same variable, but could be categorical in one study and continuous in another
Eg, pain – continuous: 0-10 mmVAS, 1-5 Likertdichotomous: ‘moderate’, ‘severe’
RevMan: outcomes are entered after the comparisons have been set up
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Hierarchy of Data and Analysis Section
• Results of studies• Tabular• Fixed format• Forest plots automatically generated
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Sample Table
Hyperlink to Forest plotHyperlink to Forest plot
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Link out to Forest Plot
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Make a Plan
Sketch it out on paper Decide what you want to do before you start
entering into RevMan Document changes between the protocol and
conducting the review
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Critical Elements
Input from more than one person, including “expert”
Transparency; state post-hoc decisions Clear, consistent
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Data extraction exercise