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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 Overview Day 1 Time Session 0900 Introductions and overview of Module 3 0930 Session 1: The Critical Appraisal of Studies 1000 1030 Morning Tea Session 2: Appraising RCTs and experimental studies Session 3: Appraising observational Studies 1145 1230 1330 Lunch 1415 Session 4: Study data and data extraction 1515 1530 Afternoon tea 1600 Session 5: Protocol development 1700 End Group Work Group Work 1: Critically appraising RCTs and experimental studies. Report back Group Work 2: Critically appraising observational studies. Report back Group Work 3: Data extraction. Report back Protocol development Program Overview Day 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 1230 Lunch 1330 Session 8: Protocol Development Protocol development 1415 Session 9: Assessment MCQ Assessment 1445 Afternoon tea 1500 Session 10: Protocol Presentations 1700 End Group Work 4: MAStARI trial. Report back Protocol Presentations Session 1: The Critical Appraisal of Studies 1004 references 172 duplicates 832 references Scanned Ti/Ab 117 studies retrieved 35 studies for Critical Appraisal 715 do not meet Incl. criteria 82 do not meet Incl. criteria Why Critically Appraise? • Combining results of poor quality research may lead to biased or misleading estimates of effectiveness The Aims of Critical Appraisal • To establish validity – to establish the risk of bias Internal & External Validity Used locally? Relationship between IV and EV? Internal Validity External Validity Strength & Magnitude How internally valid is the study? Strength How large is the effect? Magnitude & Precision 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 Selection Allocation concealment Performance Blinding Population Allocation Treatment Control Exposed to Not intervention 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 Performance Allocation concealment Blinding Detection Blinding Treatment Control Exposed to intervention Not exposed 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-Effectiveness EXAMPLE 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) • • • • • Hierarchy of Evidence-Effectiveness EXAMPLE 2 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) • • • • JBI Levels of Evidence - Effectiveness Level of Evidence Effectiveness E (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-controlled 3c. 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 2 ABAB 3 ABBA 4 BABA 5 BAAB 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 Blinding Schulz, 2002 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 high quality Included studies cut off point Excluded studies poor 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 Casecontrol 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 1004 references 172 duplicates 832 references Scanned Ti/Ab 715 do not meet Incl. criteria 117 studies retrieved 82 do not meet Incl. criteria 35 studies for Critical Appraisal 9 excluded studies 26 studies incl. in review Data most frequently extracted 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 = .5 That 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. 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 ‘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 = 4.5 Thus patients on magnesium therapy are 4 times more likely to feel better on magnesium rather than placebo 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 Issues with RR – defining success Success Failure Treatment A Treatment B 0.96 0.04 0.99 0.01 • 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 therapy and placebo: comparing magnesium ‘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 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 (to benefit) = 1 / 0.62 = 1.61 ~2 Thus 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 = 1 That 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 = 4.0 Odds of improvement on placebo = 3/14 = 0.21 Odds ratio (of Mg2+ vs placebo) = 4.0 / 0.21 = 19.0 Therefore, 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 Overview Day 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 1230 Lunch 1330 Session 8: Protocol Development Protocol development 1415 Session 9: Assessment MCQ Assessment 1445 Afternoon tea 1500 Session 10: Protocol Presentations 1700 End Group Work 4: MAStARI trial. Report back Protocol Presentations 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 Metasynthesis/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 1004 references Meta Analysis 172 duplicates 832 references Scanned Ti/Ab 117 studies retrieved 35 studies for Critical Appraisal 715 do not meet Incl. criteria 82 do not meet Incl. criteria 9 excluded studies 26 studies incl. in review 6 studies incl. in meta analysis 20 studies incl. in narrative 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 The Meta-view Graph Results of different studies combined Favours treatment No effect Favours control Heterogeneity • Is it appropriate to combine or pool results from various studies? • Different methodologies? • Different outcomes measured? • Problem greater in observational then clinical studies Heterogeneity Difference between studies Favours treatment No effect Favours control 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) Insufficient Power Studies too small to detect any effect Favours treatment No effect Favours control 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 Example subgroup analysis Taken from Egger, M. et al. BMJ 1998;316:140-144 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 Assessment and Review Instrument (MAStARI) Group Work 4 MAStARI Trial and Meta Analysis Session 8: Protocol development Session 9: Assessment Session 10: Protocol Presentations