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Appraisal, Extraction and Pooling of Quantitative Data for Reviews of Effects . - from experimental, observational and descriptive studies. Introduction. Recap of Introductory module Developing a question (PICO) Inclusion Criteria Search Strategy Selecting Studies for Retrieval - PowerPoint PPT Presentation
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Appraisal, Extraction and Pooling of Quantitative Data for Reviews of Effects
- from experimental, observational and descriptive studies
Introduction• Recap of Introductory
module – Developing a question (PICO)– Inclusion Criteria– Search Strategy– Selecting Studies for Retrieval
• This Module considers how to appraise, extract and synthesize evidence from experimental, observational and descriptive studies.
Program OverviewDay 1
Time Session Group Work0900 Introductions and overview of Module 3
0930 Session 1: The Critical Appraisal of Studies
1000 Morning Tea1030 Session 2: Appraising RCTs and experimental
studiesGroup Work 1: Critically appraising RCTs and experimental studies. Report back
1145 Session 3: Appraising observational Studies
1230 Lunch1330 Group Work 2: Critically appraising
observational studies. Report back
1415 Session 4: Study data and data extraction
1515 Afternoon tea1530 Group Work 3: Data extraction. Report back
1600 Session 5: Protocol development Protocol development1700 End
Program OverviewDay 2
Time Session Group Work
0900 Overview of Day 1
0915 Session 6: Data analysis and meta-analysis
1030 Morning Tea
1100 Session 7: Appraisal extraction and synthesis using JBI MAStARI
Group Work 4: MAStARI trial.Report back
1230 Lunch
1330 Session 8: Protocol Development Protocol development
1415 Session 9: Assessment MCQ Assessment
1445 Afternoon tea
1500 Session 10: Protocol Presentations Protocol Presentations
1700 End
Session 1: The Critical Appraisal of Studies
Why Critically Appraise?
• Combining results of poor quality research may lead to biased or misleading estimates of effectiveness
1004 references
832 referencesScanned Ti/Ab
172 duplicates
117 studiesretrieved
715 do not meetIncl. criteria
82 do not meetIncl. criteria
35 studies forCritical Appraisal
The Aims of Critical Appraisal
• To establish validity– to establish the risk of bias
Internal & External Validity
Internal Validity
External Validity
Relationship between IV and EV?
Used locally?
Strength & Magnitude
Strength Magnitude & Precision
How internally valid is the
study?
How large is the effect?
Clinical Significance and Magnitude of Effect
• Pooling of homogeneous studies of effect or harm• Weigh the effect with cost/resource of change• Determine precision of estimate
Assessing the Risk of Bias
• Numerous tools are available for assessing methodological quality of clinical trials and observational studies.
• JBI requires the use of a specific tool for assessing risk of bias in each included study.
• ‘High quality’ research methods can still leave a study at important risk of bias. (e.g. when blinding is impossible)
• Some markers of quality are unlikely to have direct implications for risk of bias (e.g ethical approval, sample size calculation)
Sources of Bias
• Selection• Performance• Detection• Attrition
Selection Bias
• Systematic differences between participant characteristics at the start of a trial
• Systematic differences occur during allocation to groups
• Can be avoided by concealment of allocation of participants to groups
Type of bias Quality assessment
Population
Allocation
Selection Allocation concealment
Treatment Control
Performance Bias
• Systematic differences in the intervention of interest, or the influence of concurrent interventions
• Systematic differences occur during the intervention phase of a trial
• Can be avoided by blinding of investigators and/or participants to group
Type of bias Quality assessment
Population
Allocation
Selection Allocation concealment
Treatment Control
Performance Blinding Exposed to intervention
Not exposed
Detection Bias
• Systematic differences in how the outcome is assessed between groups
• Systematic differences occur at measurement points during the trial
• Can be avoided by blinding of outcome assessor
Type of bias Quality assessment
Population
Allocation
Selection Allocation concealment
Treatment Control
Performance Blinding Exposed to intervention
Not exposed
Detection Blinding Population Population
Attrition Bias
• Systematic differences in withdrawals and exclusions between groups
• Can be avoided by:– Accurate reporting of losses and reasons for withdrawal– Use of ITT analysis
Type of bias Quality assessment
Population
Allocation
Selection Allocation concealment
Treatment Control
Performance Blinding Exposed to intervention
Not exposed
Detection Blinding Population Population
Attrition ITT follow up Follow up Follow up
Ranking the “Quality” of Evidence of Effectiveness
• To what extent does the study design minimize bias/demonstrate validity
• Generally linked to actual study design in ranking evidence of effectiveness
• Thus, a “hierarchy” of evidence is most often used, with levels of quality equated with specific study designs
Hierarchy of Evidence-EffectivenessEXAMPLE 1
• Grade I - systematic reviews of all relevant RCTs.• Grade II - at least one properly designed RCT• Grade III-1 - controlled trials without randomisation• Grade III-2 - cohort or case control studies• Grade III-3 - multiple time series, or dramatic results from
uncontrolled studies • Grade IV - opinions of respected authorities & descriptive
studies. (NH&MRC 1995)
• Grade I - systematic review of all relevant RCTs• Grade II - at least one properly designed RCT• Grade III-1 - well designed pseudo-randomised controlled trials• Grade III-2 - cohort studies, case control studies, interrupted
time series with a control group• Grade III-3 - comparative studies with historical control, two
or more single-arm studies, or interrupted time series without control group
• Grade IV - case series (NH&MRC 2001)
Hierarchy of Evidence-EffectivenessEXAMPLE 2
JBI Levels of Evidence - Effectiveness
Level of Evidence
EffectivenessE (1-4)
1 SR (with homogeneity) of experimental studies (e.g. RCT with concealed allocation)OR 1 or more large experimental studies with narrow confidence intervals
2 One or more smaller RCTs with wider confidence intervals OR Quasi-experimental studies (e.g. without randomisation)
3 3a. Cohort studies (with control group)3b. Case-controlled3c. Observational studies (without control groups)
4 Expert opinion, or based on physiology, bench research or consensus
The Critical Appraisal Process
• Every review must set out to use an explicit appraisal process. Essentially,– A good understanding of research design is required in
appraisers; and– The use of an agreed checklist is usual.
Session 2: Appraising RCTs and experimental studies
RCTs• RCTs and quasi (pseudo) RCTs provide the most robust form
of evidence for effects– Ideal design for experimental studies
• They focus on establishing certainty through measurable attributes
• They provide evidence related to:– whether or not a causal relationship exists between a stated
intervention, and a specific, measurable outcome, and– the direction and strength of the relationship
• These characteristics are associated with the reliability and generalizability of experimental studies
Randomised Controlled Trials
• Evaluate effectiveness of a treatment/therapy/intervention
• Randomization critical• Properly performed RCTs reduce bias, confounding
factors, and results by chance
Experimental studies
• Three essential elements– Randomisation (where possible)– Researcher-controlled manipulation of the independent
variable– Researcher control of the experimental situation
Other experimental studies
• Quasi-experiments without a true method of randomization to treatment groups
• Quasi experiments– Quasi-experimental designs without control groups– Quasi-experimental designs that use control groups but not
pre-tests– Quasi-experimental designs that use control groups and
pre-tests
Sampling
• Selecting participants from population• Inclusion/exclusion criteria• Sample should represent the population
Sampling Methods
• Probabilistic (Random) sampling • Consecutive• Systematic• Convenience
Randomization
Randomization Issues
• Simple methods may result in unequal group sizes– Tossing a coin or rolling a dice– Block randomization
• Confounding factors due to chance imbalances– stratification – prior to randomization– ensures that important baseline characteristics are even
in both groups
Block Randomization• All possible combinations ignoring unequal
allocation
1 AABB 4 BABA2 ABAB 5 BAAB3 ABBA 6 BBAA
• Use table of random numbers and generate allocation from sequence e.g. 533 2871
• Minimize bias by changing block size
Stratified Randomization
Blinding
• Method to eliminate bias from human behaviour• Applies to participants, investigators, assessors etc• Blinding of allocation• Single, double and triple blinded
Schulz, 2002
Blinding
Intention to Treat• ITT analysis is an analysis based on the initial treatment
intent, not on the treatment eventually administered. • Avoids various misleading artifacts that can arise in
intervention research. – E.g. if people who have a more serious problem tend to drop out at a
higher rate, even a completely ineffective treatment may appear to be providing benefits if one merely compares those who finish the treatment with those who were enrolled in it.
• Everyone who begins the treatment is considered to be part of the trial, whether they finish it or not.
Minimizing Risk of Bias
• Randomization• Allocation• Blinding• Intention to treat (ITT) analysis
Appraising RCTs/quasi experimental studies JBI-MAStARI Instrument
Assessing Study Quality as a Basis for Inclusion in a Review
Included studies
Excluded studies
poor quality
cut off point
high quality
Group Work 1
• Working in pairs, critically appraise the two papers in your workbook
• Reporting Back
Session 3: Appraising Observational Studies
Rationale and potential of observational studies as evidence
• Account for majority of published research studies • Need to clarify what designs to include• Need appropriate critical appraisal/quality assessment
tools• Concerns about methodological issues inherent to
observational studies– Confounding, biases, differences in design– Precise but spurious results
Appraisal of Observational Studies
• Critical appraisal and assessment of quality is often more difficult than RCTs.
• Using scales/checklists developed for RCTs may not be appropriate
• Methods and tools are still being developed and validated
• Some published tools are available
Confounding
• The apparent effect is not the true effect• May be other factors relevant to outcome in
question• Can be important threat to validity of results• Adjustments for confounding factors can be made -
multivariate analysis• Authors often look for plausible explanation for
results
Bias
• Selection bias– differ from population with same condition
• Follow up bias– attrition may be due to differences in outcome
• Measurement/detection bias– knowledge of outcome may influence assessment of
exposure and vice versa
Observational Studies - Types
• Cohort studies• Case-control studies• Case series/case report• Cross-sectional studies
Cohort Studies
• Group of people who share common characteristic• Useful to determine natural history and incidence of
disorder or exposure• Two types
– prospective (longitudinal)– retrospective (historic)
• Aid in studying causal associations
Prospective Cohort Studies
Taken from Tay & Tinmouth, 2007
Prospective Cohort Studies
• Longitudinal observation through time• Allows investigation of rare diseases or long latency
• Expensive• Increased likelihood of attrition• Long time to see useful data
Retrospective Cohort Studies
Taken from Tay & Tinmouth, 2007
Retrospective Cohort Studies
• Mainly data collection• No follow up through time• Cheaper, faster
Case-Control Studies
• Cases’ already have disease/condition• Controls’ don’t have disease/condition• Otherwise matched to control confounding• Frequently used• Rapid means of study of risk factors• Sometimes referred to as retrospective study
Case-Control Studies
Biomedical Library, University of Minnesaota, 2002
Case-Control Study
• Inexpensive• Little manpower required• Fast• No indication of absolute risk
Case series/Case reports
• Tracks patients given similar treatment– prospective
• Examines medical records for exposure and outcome– retrospective
• Detailed report of individual patient• May identify new diseases and adverse effects
Case series/Case reports
Cross-sectional Studies• Takes ‘slice’ or ‘snapshot’ of target group• Frequency and characteristics of disease/variables
in a population at a point in time• Often use survey research methods• Also called prevalence studies
Appraising comparable Cohort and Case-control studies JBI-MAStARI Instrument
Appraising descriptive/case series studies JBI-MAStARI Instrument
Group Work 2
• Working in pairs:– critically appraise the cohort study in your workbook– critically appraise the case control study in your
workbook• Reporting Back
Session 4: Study data and Data Extraction
Considerations in Data Extraction
• Source - citation and contact details• Eligibility - confirm eligibility for review• Methods - study design, concerns about bias• Participants - total number, setting, diagnostic criteria • Interventions - total number of intervention groups• Outcomes - outcomes and time points• Results - for each outcome of interest: sample size, etc• Miscellaneous - funding source, etc
Quantitative Data Extraction
• The data extracted for a systematic review are the results from individual studies specifically related to the review question.
• Difficulties related to the extraction of data include:– different populations used– different outcome measures– different scales or measures used– interventions administered differently– reliability of data extraction (i.e: between reviewers)
Minimising Error in Data Extraction
• Strategies to minimise the risk of error when extracting data from studies include:– utilising a data extraction form that is developed specifically
for each review– pilot testing the extraction form prior to commencement of
the review– training and assessing data extractors– having two people extract data from each study– blinding extraction before conferring
Data most frequently extracted
1004 references
832 referencesScanned Ti/Ab
172 duplicates
117 studiesretrieved
715 do not meetIncl. criteria
82 do not meetIncl. criteria
35 studies forCritical Appraisal
26 studies incl.in review
9 excludedstudies
Outcome Data: Effect of Treatment or Exposure
• Dichotomous– Effect/no effect– Present/absent
• Continuous– Interval or ratio level data– BP, HR, weight, etc
What do you want to know?
• Is treatment X more effective than treatment Y?• Is exposure to X more likely to result in an outcome
or not?• How many people need to receive an intervention
before someone benefits or is harmed?
Risk
• Risk =# times something happens
# opportunities for it to happen• “Risk” of birthing baby boy?
– One boy is born for every 2 opportunities: 1/2 = .5That is: 50% probability (risk) of having a boy
• One of every 100 persons treated, has a side-effect, 1/100 = .01
Relative Risk (RR)• Ratio of risk in exposed group to risk in not exposed
group (Pexposed/Punexposed)– The RR of anaemia during pregnancy = the risk of
developing anaemia for pregnant women divided by the risk of developing anaemia for women who are not pregnant.
– The RR of further stroke for patients who have had a stroke = risk of a stroke within one year post stroke divided by the risk of having a stroke in one year for a similar group of patients who have not had a stroke.
‘Risk’ of improvement on magnesium = 12/ 15 = 0.80‘Risk’ of improvement on placebo = 3/ 17 = 0.18 Relative risk (of improvement on Mg2+ therapy vs placebo) = 0.80/0.18 = 4.5Thus patients on magnesium therapy are 4 times more likely to feel better on magnesium
rather than placebo
For example• A trial examined whether patients with chronic fatigue syndrome
improved 6 weeks after treatment with i.m. magnesium. The group who received the magnesium were compared to a placebo group and the outcome was feeling better
Interpreting Risk
• What does a relative risk of 1 mean? – That there is no difference in risk in the two groups. – In the magnesium example it would mean that patients are
as likely to “feel better” on magnesium as on placebo– If there was no difference between the groups the
confidence interval would include 1• It is important to know whether relative or absolute risk
is being presented as this influences the way in which it is interpreted
Treatment A Treatment B
SuccessFailure
0.960.04
0.990.01
Issues with RR – defining success
• If the outcome of interest is success then RR=0.96/0.99=0.97• If the outcome of interest is failure then RR=0.04/0.01=4
Absolute Risk Difference
• Is the absolute additional risk of an event due to an exposure.– Risk in exposed group minus risk in unexposed (or
differently exposed group).
• Absolute risk reduction (ARR) = Pexposed - Punexposed • If the absolute risk is increased by an exposure we
sometimes use the term Absolute Risk Increase (ARI)
For example• From the previous example of comparing magnesium therapy and placebo:
‘Risk’ of improvement on magnesium = 12/ 15 = 0.80‘Risk’ of improvement on placebo = 3/ 17 = 0.18 Absolute risk reduction = 0.80 - 0.18 = 0.62
Number Needed to Treat
• The additional number of people you would need to give a new treatment to in order to cure one extra person compared to the old treatment.
• For a harmful exposure, the number needed to harm is the additional number of individuals who need to be exposed to the risk in order to have one extra person develop the disease, compared to the unexposed group.– Number needed to treat = 1 / ARR– Number needed to harm = 1 / ARR, ignoring negative sign.
For exampleFrom the previous example of comparing magnesium therapy and placebo:
‘Risk’ of improvement on magnesium = 12/ 15 = 0.80‘Risk’ of improvement on placebo = 3/ 17 = 0.18 Absolute risk reduction = 0.80 - 0.18 = 0.62Number needed to treat (to benefit) = 1 / 0.62 = 1.61 ~2Thus on average one would give magnesium to 2 patients in order to expect one extra patient (compared to placebo) to feel better
Odds
• Odds =# times something happens# times it does not happen
• What are the odds of birthing a boy? – For every 2 births, one is a boy and one isn’t
1/1 = 1That is: odds are even
• One of every 100 persons treated, has a side-effect, 1/99 = .0101
Odds Ratio• Ratio of odds for exposed group to the odds for not
exposed group: {Pexposed / (1 - Pexposed)}
{Punexposed / (1 - Punexposed)}
For example• From the previous example of comparing magnesium therapy and placebo:
Odds of improvement on magnesium = 12/3 = 4.0Odds of improvement on placebo = 3/14 = 0.21 Odds ratio (of Mg2+ vs placebo) = 4.0 / 0.21 = 19.0Therefore, improvement was 19 times more likely in the Mg2+ group than the placebo group.
Relative Risk and Odds Ratio
• The odds ratio can be interpreted as a relative risk when an event is rare and the two are often quoted interchangeably
• This is because when the event is rare (b+d)→ d and (a+c)→c. – Relative risk = a(a+c) / b(b+d)
– Odds ratio = ac / bd
Relative Risk and Odds Ratio
• For case-control studies it is not possible to calculate the RR and thus the OR is used.
• For cohort and cross-sectional studies, both can be derived.
• OR have mathematical properties which makes them more often quoted for formal statistical analyses
Continuous data
• Means, averages, change scores etc.– E.g. BP, plasma protein concentration,
• Any value often within a specified range• Mean, Standard deviation, N
• Often only the standard error, SE, presented• SD = SE x √ N
MAStARI Data Extraction Instrument
Group Work 3
• Working in pairs:– Extract the data from the two papers in your workbook
• Reporting Back
Session 5: Protocol development
Program OverviewDay 2
Time Session Group Work
0900 Overview of Day 1
0915 Session 6: Data analysis and meta-analysis
1030 Morning Tea
1100 Session 7: Appraisal extraction and synthesis using JBI MAStARI
Group Work 4: MAStARI trial.Report back
1230 Lunch
1330 Session 8: Protocol Development Protocol development
1415 Session 9: Assessment MCQ Assessment
1445 Afternoon tea
1500 Session 10: Protocol Presentations Protocol Presentations
1700 End
Overview
• Recap Day 1– Critical appraisal– Study design– Type of studies
(experimental and observational)
– Data extraction• Today focus is on data
analysis and synthesis.
Session 6: Data Analysis and Meta-synthesis/Meta-analysis
General Analysis - What Can be Reported and How
– What interventions/activities have been evaluated– The effectiveness/appropriateness/feasibility of the
intervention/activity– Contradictory findings and conflicts– Limitations of study methods– Issues related to study quality– The use of inappropriate definitions– Specific populations excluded from studies– Future research needs
Meta Analysis1004 references
832 referencesScanned Ti/Ab
172 duplicates
117 studiesretrieved
715 do not meetIncl. criteria
82 do not meetIncl. criteria
35 studies forCritical Appraisal
26 studies incl.in review
6 studies incl.in meta analysis
20 studies incl.in narrative
9 excludedstudies
Statistical methods for meta-analysis
• Quantitative method of combining results of independent studies
• Aim is to increase precision of overall estimate• Investigate reasons for differences in risk estimates
between studies• Discover patterns of risk amongst studies
When is meta-analysis useful?
• If studies report different treatment effects.• If studies are too small (insufficient power) to detect
meaningful effects.• Single studies rarely, if ever, provide definitive
conclusions regarding the effectiveness of an intervention.
When meta-analysis can be used
• Meta analysis can be used if studies:– have the same population– use the same intervention administered in the same way.– measure the same outcomes
• Homogeneity– studies are sufficiently similar to estimate an average
effect.
Calculating an Overall Effect Estimate
• Odds Ratio – for dichotomous data eg. the outcome present or absent– 51/49 = 1.04– (no difference between groups = 1)
• Weighted mean difference– Continuous data, such as weight – (no difference between groups = 0)
• Confidence Interval– The range in which the real result lies, with the given degree of
certainty
Confidence Intervals
• Confidence intervals are an indication of how precise the findings are
• Sample size greatly impacts the CI– the larger the sample size the smaller the CI, the greater
the power and confidence of the estimate
CIs indicate:
• When calculated for OR, the CI provides the upper and lower limit of the odds that a treatment may or may not work
• If the odds ratio is 1, odds are even and therefore, not significantly different – recall the odds of having a boy
Favours treatment Favours controlNo effect
Results of different studies combined
The Meta-view Graph
Heterogeneity
• Is it appropriate to combine or pool results from various studies?
• Different methodologies?• Different outcomes measured?• Problem greater in observational then clinical
studies
Favours treatment Favours controlNo effect
Difference between studies
Heterogeneity
Tests of Heterogeneity
• Measure extent to which observed study outcomes differ from calculated study outcome
• Visually inspect Forest Plot. Size of CI• 2 Test for homogeneity or Q Test can be used
– low power (use p < 0.1 or 0.2)
Favours treatment Favours controlNo effect
Studies too small to detect any effect
Insufficient Power
Meta-analysis
• Overall summary measure is a weighted average of study outcomes.
• Weight indicates influence of study• Study on more subjects is more influential• CI is measure of precision• CI should be smaller in summary measure
Subgroup analysis
• Subgroup analysis • Some participants, intervention or outcome you thought
were likely to be quite different to the others• Should be specified in advance in the protocol• Only if there are good clinical reasons
• Two types• Between trial – trials classified into subgroups• Within trial – each trial contributes to all subgroups
Taken from Egger, M. et al. BMJ 1998;316:140-144
Example subgroup analysis
Sensitivity Analysis
• Exclude and/or include individual studies in the analysis
• Establish whether the assumptions or decisions we have made have a major effect on the results of the review
• ‘Are the findings robust to the method used to obtain them?’
Meta-analysis
• Statistical methods– Fixed effects model– Random effects model
Fixed Effects Model
• All included studies measure same outcome• Assume any difference observed is due to chance
– no inherent variation in source population– variation within study, not between studies
• Inappropriate where there is heterogeneity present• CI of summary measure reflects variability between
patients within sample
Random Effects Model• Assumed studies are different and outcome will
fluctuate around own true value– true values drawn randomly from population– variability between patients within study and from
differences between studies• Overall summary outcome is estimate of mean from
which sample of outcomes was drawn• More commonly used with observational studies due
to heterogeneity
Random Effects Model
• Summary value will often have wider CI than with fixed effects model
• Where no heterogeneity results of two methods will be similar
• If heterogeneity present may be best to do solely narrative systematic review
Session 7: Appraisal, extraction and synthesis
using JBI-MAStARI
Meta Analysis of Statistics Assessmentand Review Instrument (MAStARI)
Group Work 4
MAStARI Trial and Meta Analysis
Session 8: Protocol development
Session 9: Assessment
Session 10: Protocol Presentations