In NNTbiomarker: Calculate Design Parameters for Biomarker Validation Studies

r input$biomarkerReportTitle

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Step 1. r stepsTableInitial[1, "SteppingStone"]:

• r stepsTableInitial[1,"Question"]

r ClinicalScenario.

Step 2. r stepsTableInitial[2, "SteppingStone"] :

• r stepsTableInitial[2,"Question"]

The biomarker is intended to help r input$who, by identifying who should receive r input$Option_Treat and who should receive r input$Option_Wait.

Step 3. r stepsTableInitial[3, "SteppingStone"]:

• r stepsTableInitial[3,"Question"]

Currently, the proportion of patients who should receive prevalence is number needed to treat to help one (NNT) is The biomarker test will be useful can create a clinical consensus supporting using the test for clinical decisions. if the NNT among test-positive patients, NNT_Pos, is less than NNT_Lower = r NNTlower, and if the NNT among test-negative patients, NNT_Neg, is greater than NNT_Upper = r NNTupper.

Therefore we choose targets NNT_Pos = r input$NNTpos and NNT_Neg = r input$NNTneg. This performance should suffice to create a clinical consensus supporting using the test for clinical decisions. These values correspond to positive predictive value = PPV = r 1/input$NNTpos, and negative predictive value = NPV = r 1-1/input$NNTneg.

Step 4. r stepsTableInitial[4, "SteppingStone"]:

• r stepsTableInitial[4,"Question"]

If the biomarker test achieves these predictive values, the benefit to patients will be r input$SpecificBenefit.

Step 5. r stepsTableInitial[5, "SteppingStone"]:

• r stepsTableInitial[5, "Question"]

The retrospective study will recruit r input$NpatientsProspective patients. r input$follow_up. If the test divides the r input$NpatientsProspective patients into roughly r input$percentPositive% positive and r 100 - input$percentPositive% negative, and if the estimates match the hoped-for values NNT_Pos = r input$NNTpos and NNT_Neg = r input$NNTneg, then the confidence intervals would be (r round(digits=3, rValues$PPV_ProspectiveInterval)) for PPV, and (r round(digits=3, rValues$NPV_ProspectiveInterval)) for NPV, or equivalently (r round(digits=3, rValues$NNTpos_ProspectiveInterval)) for NNT_Pos, and (r round(digits=3, rValues$NNTneg_ProspectiveInterval)) for NNT_Neg.

r input$ProspectiveStudyNotes

Step 6. r stepsTableInitial[6, "SteppingStone"]:

• r stepsTableInitial[6,"Question"]

The proportion of patients r input$BestToTreatDescription is assumed to be r round(100*input$prevalence)%. Combining that with the target PPV and NPV, the required sensitivity (SN) and specificity (SP) are r rValues$sensitivityPercent% and r rValues$specificityPercent%, respectively (contra-Bayes Theorem). To get a sense of the accuracy of anticipated estimates in the retrospective (case/control) portion of the study, we consider anticipated results for samples sizes r input$samplesizeCases cases and r input$samplesizeControls controls. For example, if the estimates SN = r round(input$samplesizeCases* rValues$sensitivity)/r input$samplesizeCases = r round(100*input$samplesizeCases* rValues$sensitivity/input$samplesizeCases) % and SP = r round(input$samplesizeControls* rValues$specificity)/r input$samplesizeControls = r round(100*input$samplesizeControls* rValues$specificity/input$samplesizeControls) % are observed, then the corresponding confidence intervals will be (r round(digits=3, rValues$Se_RetrospectiveInterval)) for SN, and (r round(digits=3, rValues$Sp_RetrospectiveInterval)) for SP. For NNT_Pos and for NNT_Neg, the Bayes predictive intervals will be (r round(digits=3, rValues$NNTpos_RetrospectiveInterval)) for NNT_Pos , and (r round(digits=3, rValues$NNTneg_RetrospectiveInterval)) for NNT_Neg . (These predictive intervals derive from assuming independent Jeffreys priors for SN and SP, sampling from joint independent posteriors incorporating the anticipated results, and applying Bayes theorem).

NNTbiomarker documentation built on May 1, 2019, 11:15 p.m.