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Systema(c review and meta-‐analysis of Diagnos(c Test Studies
Kuan-‐Fu Chen CGMH EM
SR/MA workshop January 8, 2013
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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Difference of Dx SR/MA
• ID studies, assessing bias • Method to combine results • Paired of summary sta(s(cs to pool • Design of diagnos(c studies
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Diagnos(c studies
• Defini(on – Dia + gnosis = apart/separated + knowledge – Dia: Greek ‘through’ – Gnosis: Greek ‘knowledge’ – To reduce uncertainty
• Purposes – Screening, triage, add-‐on or replacement tests, predict outcomes or monitor dz process
– Purpose must be considered during evalua(on
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Phases of Dx test evalua(on
• Prelim evalua(on (case-‐control) – I: whether results different for pts ± dz – II: whether dz more likely to have + results
• Accuracy & probability of condi(on (cross-‐sec(onal/cohort study) – III: how well dis(nguishes btw pts ± dz with suspicion – IV: how informa(ve as add-‐on
• Health outcomes (Randomized or before-‐ader study) – V: whether leads to beeer outcomes – VI: acceptable costs
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Phases of biomarker evalua(on
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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Steps of (diagnos(c) SR/MA
• Formula(ng a focused ques(on • Reviewing guidelines • ID databases/sources of studies • Run searches and save cita(ons • First screen by two reviewers • Second screen by two reviewers • Data extrac(on and quality assessment • Data analyses
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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PICO(S) for of diagnos(c studies • Popula(on
– What are the characteris(cs of the pa(ents? – What is the condi(on that may be present?
• Interven(on (diagnos(c test) – Which diagnos(c test am I considering?
• Comparison – What is the diagnos(c gold standard?
• Outcome – How likely is the test to predict/rule out this condi(on?
• Study design/sekng – What study design would provide the best level of evidence? – What clinical sekng would this study apply to?
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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Guidelines for (SR/MA) studies
• QUA D A S – QUAlity of Diagnos(c Accuracy research Studies in SR
• STA R D – STAndard for Repor(ng of Diagnos(c accuracy
• RE MARK – REpor(ng recommenda(ons for tumor MARKer prognos(c studies
• P R I S M – Preferred Repor(ng Items for SR/MA
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QUADAS (2)
• Four phases to establish tool for each review • Four key domains to review • Three sec(ons in each domain for risk of bias
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QUADAS – phases
• Review ques(ons • Review specific tailoring • Flow diagram • Judgments on bias and applicability
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QUADAS – phases
• Review ques(ons – Four-‐part ques(on:
• (P) Pa(ents, • (I) Index test(s), • (C) Reference standard, and • (O) Target condi(on
– Diagnos(c pathway: • Sekng, inten(on, pa(ent presenta(on, and/or prior tes(ng
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QUADAS – phases
• Review specific tailoring
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QUADAS – phases
• Flow diagram
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QUADAS – phases
• Judgments on bias and applicability – Three sec(ons in each domain for risk of bias
• Informa(on to support • Signaling ques(ons • Judgment of risk of bias
– Applicability • Similar but not including signaling ques(ons • Refer to first phase (review ques(on)
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QUADAS – domains
• Pa(ent selec(on • Index test • Reference standard • Flow and (ming
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QUADAS – domains • Pa(ent selec(on – Signaling ques(ons
1. Was a consecu(ve or random sample of pa(ents enrolled? 2. Was a case-‐control design avoided? 3. Did the study avoid inappropriate exclusions?
– Risk of bias • Spectrum bias (one of selec(on biases) (lab)? • Excluding difficult to diagnose; liele diagnosis uncertainty:
– overop(mis(c • Exclusion of “red flags”: underes(ma(on • Selec(on towards more severe manifesta(ons
– Increase prevalence – Bias in any direc(on
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QUADAS – domains
• Pa(ent selec(on – Applicability concerns
• Target vs. Study popula(on: generalizability • Any discrepancy re: severity, demographics, presence of differen(al diagnosis, comorbidity, sekng and previous tes(ng protocol
– Supplemental: prospec(ve vs. Retrospec(ve? • Retrospec(ve:
– No verifica(on of Dz status – Same reference standard for all? – Same assessors for reference standard for all?
Target'Population
Study&population
Target'Population
Study&population
Target'Population
Study&population
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QUADAS – domains
• Index test – Signaling ques(ons
1. Were the index test results interpreted without knowledge of the results of the reference standard?
2. If a threshold was used, was it pre-‐specified?
– Risk of bias • Ques(on 1: blinding, subjec(vity • Ques(on 2: overop(mis(c
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QUADAS – domains
• Index test – Applicability concerns
• Same index tests?
– Supplemental: (QUADAS 1) • Reproducibility: for all tests, different phases
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QUADAS – domains
• Reference standard – Signaling ques(ons
1. Is the reference standard likely to correctly classify the target condi(on?
2. Were the reference standard results interpreted without knowledge of the results of the index test?
– Risk of bias • Ques(on 1: direc(on? Misclassifica(on bias (textbook?) – Bias confounding and role of chance slide
• Ques(on 2: similar to index test, Incorpora(on bias
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QUADAS – domains
• Reference standard – Applicability concerns:
• target condi(on defined by the reference standard does not match the ques(on? • Example: U/C for UTI with different cutoffs
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QUADAS – domains
• Flow and (ming – Signaling ques(ons
1. Was there an appropriate interval between index test and reference standard?
2. Did all pa(ents receive the same reference standard? 3. Were all pa(ents included in the analysis?
– Risk of bias • Ques(on 1: misclassifica(on bias (if delay or treatment started). Direc(on? Acute vs. Chronic
• Ques(on 2: Verifica(on bias: overes(mate • Ques(on 3: lost to follow-‐up: Direc(on? Could be either direc(on (selec(on bias)
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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Literature searching
• Electronic database – Less produc(ve and more difficult
• Different strategies – Different indexing terms/text words – MeSH: diagnosis – Textwords: specificity, sensi(vity
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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Evalua(on of studies
• Selec(on of sample. – Ideal: consecu(ve/randomly selected
• Reference test • Blinding • Quality repor(ng • Evidence of bias: – table of rela(ve DOR in different study design
• Incorpora(ng QA in SR: Checklist
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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Data extrac(on • Accuracy expression
– Sensi(vity/specificity: Based on cut-‐off – ROC curve – PPV → influence of prevalence
• Choice of parameters: – May consider DOR instead of Sensi(vity/specificity, LR
• DOR (Diagnos(c Odds Ra(o) also = LR(+)/LR(-‐) – DOR 25: LR(+) 5, LR(-‐) 0.2, DOR 100: LR(+) 10, LR(-‐) 0.1
• However, discarded informa(on • Predic(ve value: high heterogeneity→prevalence varia(on
– Post-‐test probability can be used instead • Examples of abstract/ar(cle review form
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Objec(ves
• Differences of diagnos(c SR/MA? • Steps of diagnos(c SR/MA • Formula(ng a focused ques(on • Reviewing guidelines: QUADAS • Literature searching • Evalua(on of studies • Data extrac(on • Data synthesis
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Data synthesis
Meta-regression DOR on
characteristics, investigate source of
heterogeneity
LM methodtest of
asymmetry,SROC or pool ROC curves
Pool sensitivity & specificity
Test of cut-off effect
Test of heterogeneity
heterogenous
homogenous
no
yes sym
Bayesian adaptation
SROC
asym
SROC: summary ROC LM method: Lieenburg – Moses regression method DOR: diagnos(c odds ra(o
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WORKSHOP TIME! Systema(c review and meta-‐analysis of Diagnos(c Test Studies
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Objec(ves
• Understand and workout poten(al bias during diagnos(c SR/MA – Spectrum bias – Misclassifica(on bias – Incorpora(on bias – Lost of follow up – Selec(ng threshold – verifica(on bias
• Use QUADAS-‐2 form to evaluate one study
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Selec(on of pa(ent (spectrum bias)
• Excluding difficult to diagnose – Overop(mis(c or underes(ma(ng, why?
• Excluding ‘red flag’ – Overop(mis(c or underes(ma(ng, why?
• Evaluate and discuss the reasons in examples
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Example 1: Centor’s CPR
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Example 2: BNP
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Example 2: BNP
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Misclassifica(on bias
• Use those tables to discuss the reasons – PCR vs. B/C for sepsis diagnosis – PCT vs. B/C for sepsis diagnosis
• Use blood culture as example – Discuss interval and reference test issue
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Misclassifica(on bias Cases: B/C + Control: B/C -‐ Total
PCR + 100 40 150
PCR -‐ 50 60 100
150 100 250
False Sensi(vity=66% Specificity=60%
Cases: real sepsis Control: real ctrl Total
PCR + 110 40 150
PCR -‐ 40 60 100
150 100 250
True Sensi(vity=73% Specificity=60%
DifferenAal misclassificaAon bias Say Sensi(vity of B/C = 80%, specificity = 90%, and related to PCR results
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Misclassifica(on bias Cases: B/C + Control: B/C -‐ Total
PCT + 100 40 150
PCT -‐ 50 60 100
150 100 250
False Sensi(vity=66% Specificity=60%
Cases: real sepsis Control: real ctrl Total
PCT + 125 25 150
PCT -‐ 63 37 100
188 62 250
True Sensi(vity=66% Specificity=60%
Non-‐DifferenAal misclassificaAon bias Say Sensi(vity of B/C = 80%, specificity = 75%, and not related to PCT results
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Incorpora(on bias
• Example: SIRS vs. WBC for sepsis diagnosis – Overop(mis(c or underes(ma(ng, why?
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Lost of follow up
• Example: transferred to other hospital – Overop(mis(c or underes(ma(ng, why?
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Selec(ng threshold
• Example: – Post-‐hoc determina(on of BUN/Cre > 15 as risk factor in acute stroke
– Overop(mis(c or underes(ma(ng, why?
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Verifica(on bias
• Example: PSA – Verifica(on and incorpora(on biases in studies assessing screening tests: prostate-‐specific an(gen as an example (Gupta & Roehrborn)
– Overop(mis(c or underes(ma(ng, why?
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Verifica(on bias Disease + Disease -‐ Total % Verified
PSA + 231 32 263 61%
PSA -‐ 27 54 8 37%
Total 258 86 344
Sensi(vity=90% Specificity=63%
Corrected Disease + Disease -‐ Total
PSA +
PSA -‐
Total
Sensi(vity=__% Specificity=__%
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Verifica(on bias Disease + Disease -‐ Total % Verified
PSA + 231 32 263 61%
PSA -‐ 27 54 8 37%
Total 258 86 344
Sensi(vity=90% Specificity=63%
Disease + Disease -‐ Total
PSA + 377 52 429
PSA -‐ 74 147 221
Total 451 199 650
Sensi(vity=84% Specificity=74%
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Verifica(on bias
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Verifica(on bias
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