Handling, And Judging Risk Of Bias Associated With Missing .
Handling, and judging risk of biasassociated with missing participantdata in meta-analyses of binary andcontinuous outcomesElie Akl, Shanil Ebrahim, Gordon Guyatt
No conflicts of interest to declare This work has been partly funded by theMethods Innovation Fund (MIF)
Objective To develop guidance for systematic reviewauthors on how to handle, and judge risk ofbias associated with missing participant datain meta-analyses of binary and continuousoutcomes
Trial level participant entryTrial level participant flowRandomizedMistakenlyrandomizedTrial level data collectionTrial level data analysisCollectedAppropriatelyexcludedSystematic review level data availabilitySystematic review level data analysisAdherent andfollowed-upExcludeCollectedIncludedLost ilableAccordingto trialanalysisITT, perprotocol oras MissingavailableCCA, or makeassumptions
Proposal to handle MPD For the primary analysis: exclude participantswith missing data (complete case analysis) To assess the risk of bias, and when theprimary analysis suggests important effect, wesuggest sensitivity meta-analyses makingdifferent assumptions about the outcome ofparticipants with missing dataAkl et al. PLoS One. 2013;8(2):e57132
Judging RoB dichotomous MPD Results robust to a worst case scenario missing data does not represent a risk of bias Results not robust to worst case scenario test progressively more extreme assumptionsculminating in a "worst plausible case” Important changes in results with suchsensitivity analyses suggest serious RoB
Example Meta-analysis assessing effects of probioticsfor prevention clostridium difficile-associateddiarrheaNeumann et al. Ann Intern Med. 2012 May 15;156(10)
Complete case analysis
Event rate: 1.5:1
Event rate: 3:1
Event rate: 5:1
Handling continuous MPD Strategies to combine imputations for participants with missingdata with those with complete data Progressively more stringent strategies to challenge estimatesEbrahim et al. J Clin Epidemiol. 2013 Sep;66(9):1014-1021.e1
Imputing effect & precisionMeasure of effect5 sources of data reflecting real observed mean scores inparticipants followed-up in individual trials in a meta-analysis: Ranging from:o Best mean score among intervention armso Worst mean score among control armsMeasure of precision Median SD (plausible)
Imputation strategies Developed 4 progressively more stringent imputation strategiesfor participants with missing data in both armsAssumptions about the means of participants inINTERVENTIONC: Mean score from theD: Worst mean amongcontrol arm of the same trial intervention arms4Intervention: Worst meanamong control armsControl: Best mean amongintervention armsA: Best mean amongintervention armsAssumptionsabout themeans ofparticipants inCONTROL2Intervention: Worst meanamong intervention armsControl: Best meanamong control armsB: Best mean amongthe control armsC: Mean score fromthe control arm of thesame trialE: Worst mean amongcontrol arms1Intervention and control:Mean score from the controlarm of the same trial3Intervention: Worst meanamong control armsControl: Best meanamong control arms
Combining observed & imputed data3-step method for each strategy:[1] Combine observed means and SDs of those with availabledata with imputed means and SDs for those with missing data[2] Use pooled estimates to calculate treatment effect per study[3] Perform a standard random-effects meta-analysis to pool
Application of approach: 1 Cognitive behavioural therapy (CBT) versus minimal or notreatment for depression in patients receiving disability benefits 8 RCTs: Beck Depression Inventory Median missing participant data rate 21% (range 0 to 41%)Ebrahim et al. PLoS One. 2012;7(11):e50202
Application of approach: 2 Finasteride therapy versus placebo on improvement in scalphair for men with androgenetic alopecia 8 RCTs Median missing participant data rate 14% (range 0% to 24%)Mella et al. Arch Dermatol. 2010 Oct;146(10):1141-50.
DiscussionCBT review:o Effect diminished, lost significance as strategies becamemore stringento Rate down for risk of biasFinasteride review:o Even most stringent: effect important, statistical sig remainso Do not need to rate down for risk of bias
Conclusions Approach involving progressively more stringent assumptionsabout results in participants with missing data Provides guidance on rigorously determining the extent towhich missing data increases risk of bias in systematic reviews To the extent that results change with the sensitivity analyses,risk of bias as a result of missing data increases
For the primary analysis: exclude participants with missing data (complete case analysis) To assess the risk of bias, and when the primary analysis suggests important effect, we suggest sensitivity meta-analyses making different assumptions about the outcome of participants with missing
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