依法行使权利经典课件12

Analysisandevaluationoftheevidenceofdiagnostictest

AnalysisandevaluationoftheDiagnostictestarenotjustaboutdiagnosisScreeningDeterminingseverityOptimallytherapyPrognosisMonitorDiagnostictestarenotjustaExampleCarotidultrasoundcantellyoutheseverityofthepatient’scarotidstenosis

Carotidultrasoundcantellyouthepatient’sprognosisforstrokeanddeathCarotidultrasoundcanpredictyourpatient’slikelyresponsivenesstotherapyExampleCarotidultrasoundcanBasicprinciplesofconductingdiagnosticstudiesApplythegoldstandardtodeterminewhetherornotthetargetconditionispresent

Goldstandard:ThemostrecognizedstandardforcliniciantodiagnosethetargetconditionPathologicalmeasurementOperationfindingSpecialimagingdetectionLong-termfollow-upRecognizedstandardBasicprinciplesofconductingWhatifyourtestismoregoldthanthestandardMayleadtounderestimateofthediagnosticpoweroftheevaluatingOnestrategyfordealingwiththisproblemistouselong-termfollow-upasagoldstandardWhatifyourtestismoregoldToWhomShouldtheGoldStandardBeApplied?

toeveryoneselectiveperformingthegoldstandardonpatientsmayresultin“verificationbias”or“workupbias”ToWhomShouldtheGoldStandaRecruityourparticipantsRecruitthetarget-negativeandtarget-positiveparticipantsidentifiedbygoldstandardcharacteristicofthosetowhomyouwillwanttoapplythetestinclinicalpracticeIncludingabroadspectrumofthediseasedcase：frommildlytoseverelycontrol：abroadspectrumofcompetingconditionsAnalternativeapproachisthatrecruitingaconsecutivesampleofpatientsRecruityourparticipantsRecru依法行使权利经典课件1[2]依法行使权利经典课件1[2]MeasurementproceduresSpecifyingtesttechniqueReproducibilityBlindingoftheindividualconductingorinterpretingthetesttothegoldstandardMeasurementproceduresSpecifyiSelectstatisticalprocedureCalculatingsamplesizeSelectstatisticalprocedureCa

Example:Assumingasensitivityof80%,specificityof60%ofultrasonographyfordiagnosisofcholecystolithiasis.Howmanysamplesareneeded?Example:AssumingasensitiResultevaluationindexExample:126patientsunderwentindependent,blindBNPmeasurementandechocardiographyfordiagnosisofLVD.ResultevaluationindexExample依法行使权利经典课件1[2]sensitivity：a/(a+c)=35/40=0.88,or88%specificity：d/(b+d)=29/86=0.34,or34%positivepredictivevalue(PPV)：a/(a+b)=35/92=0.38,or38%negativepredictivevalue(NPV)：d/(c+d)=29/34=0.85,or85%prevalence:(a+c)/(a+b+c+d)=40/126=0.32,or32%Pre-testodds:pre-testprobability/(1-pre-testprobability)=32%/68%=0.47positivelikelihoodratio(LR+):Sen/1-Spe=88%/(100%-34%)=1.3sensitivity：a/(a+c)=35/40=0.88依法行使权利经典课件1[2]MultilevellikelihoodratiosMultilevellikelihoodratiosStabilityoftheindexStable:Sen,SpeRelativelystable:LR+,LR-Unstable:PPV,NPV,prevalence:StabilityoftheindexStable:Receiveroperatingcharacteristiccurves(ROC)Itillustratestheperformanceofadiagnostictestwhenyouselectdifferentcut-pointstodistinguish“normal”from“abnormal”ItdemonstratesthefactthatanyincreaseinsensitivitywillbeaccompaniedbyadecreaseinspecificityThecloserthecurvegetstotheupperleftcornerofthedisplay,themoretheoverallaccuracyofthetestThecloserthecurvecomestothe45-degreediagonaloftheROCspace,thelessaccuratethetestTheareaunderthecurveprovidesanoverallmeasureofatest’saccuracyReceiveroperatingcharacterisFigAROCforBNPasadiagnostictestforLVDFigAROCfor依法行使权利经典课件1[2]ParalleltestA

testBtestResult

+++

++

++-ParalleltestAtestReductionmissdiagnosisExcludesomediseaseWhenprevalenceislow,astheprimaryscreeningmethodReductionmissdiagnosisSerialtestA

testBtestResult

+++

+-

+--SerialtestAtestSen=SenA×SenB

Spe=SpeA+(1-SpeA)SpeBMisdiagnosismaycausenuisanceeffectConfirmatorydiagnosisSen=SenA×SenB

Spe=SpeA

SerialtestwithenzymelabeledcompoundassayfordiagnosisofmyocardialinfarctionEnzymelabeledcompoundassaySenSpeCPK9657SGOT9174LDH879191

SerialtestwithenzymelabelMultivariateanalysisSENSPEMultivariateanalysisSENsinglevariableanalysissinglevariableanalysismultivariateanalysisusinglogisticregression

multivariateanalysisusingloPredictiontheprobabilityofadiseaseLogit(P)=-0.934+4.797xa+2.203xePredictiontheprobabilityofAvoidingoverfitting

Overfittingoccurswhenacomputermodelidentifiesa“chance”patternthatdiscriminatescancerpatientsfromnon-cancerpatients,perfectlyfittingthatdatasetbutnotreproducibleinotherdatasets.Onewaytoavoidingoverfittingistorandomlysplitthedataintoseparatetrainingandtestsamples.AvoidingoverfittingOverfTheEBMstepsfordiagnostictestsLookingforthemostsuitablestudypapersaccordingtotheclinicalquestionBringforwardthequestioninclinicExample2:

if

detection

of

serum

forritin

can

diagnose

Irondeficiencyanemia?Searchthecomputerinformationusingtheappositekeyword“diagnose

Irondeficiencyanemia”and“diagnostictest”and“human”TheEBMstepsfordiagnostictEvaluationofthescientificityofthepapersIfcomparedwiththegoldstandardindependentlyandblindlyExample2:Ironstainwith

myeloidbiopsyEvaluationofthescientificitIfdetectedwiththecontroltestforeveryquizzeeIfdetectedwiththecontrolVerificationbiasVerificationbiasIfthepatientsamplesincluded

abroadspectrumofthediseaseIfthediseasespectrumuniformWhat’stheobjectivequestionthattheinvestigatorconcernedaboutIfthestudysampleandthequizzeeisuniformSpectrumbias：overstatetheperformanceparameterofthediagnostictestbecauseofexcludingthe“greyzone”patientsIfthepatientsamplesincludeTheprecisionofthediagnostictestsDefinition:inthesamecondition,degreeofstabilityofachievingthesameresultwhenrepeatingtheoperationDetailedintroductiontheprocedurestandardsofreportingdiagnosticaccuracy,STARDTheprecisionofthediagnostiEstimatethesignificanceofclinicalapplicationEstimatethepre-testprobabilityPre-testprobability：EstimatingthesufferingprobabilitybeforediagnostictestMedicalrecordMedicalexaminationIndividualexperienceEpidemiologydataEstimatethesignificanceofcThepredictivevaluedependonthepre-testprobabilityofillnessThepredictivevaluedependonExplainanduseof

sensitivityandspecificityHighsensitivitytest(negativetest,ruleout)Highspecificitytest(positivetest,rulein)ExplainanduseofsensitivityUselikelihoodratio,LRLikelihoodratio：providesadirectestimateofhowmuchatestresultwillchangetheprobabilityofhavingadiseaseLR+=sensitivity/(1-specificity)Likelihoodratiounchangedwiththeprevalencerate

OvercomingdeficiencywithonlysensitivityorspecificitytoexpressUselikelihoodratio,LRLikelipost-testprobability

post-testprobability=post-testodds/（1+post-testodds）post-testodds=pre-testodds×LRpre-testodds=pre-testprobability×（1-pre-testprobability）Crudeprinciple:⑴LR>10or<0.1affirmordenial⑵5<

LR<10or0.1<

LR<0.2moderateprobability⑶2<

LR<5or0.2<

LR<0.5minorprobability⑷1<

LR<2or0.5<

LR<1unchangedpost-testprobability

post-teTheLRwithserumferritinfordiagnosisofiron-deficiencyanemiaTheLRwithserumferritinforTheLRwithserumferritinfordiagnosisofiron-deficiencyanemiaTheLRwithserumfer