sesp.from.pv.feasible: sesp.from.pv.feasible

In NNTbiomarker: Calculate Design Parameters for Biomarker Validation Studies

Description

Computes sensitivity and specificity from predictive values.

Usage

sesp.from.pv.feasible(ppv, npv, prev, feasible = TRUE)

Arguments

ppv	Positive predictive value
npv	Negative predictive value
prev	Prevalence (prior probability)
feasible	Only return results in [0,1]. Default=TRUE

Details

NNT stands for Number Needed to Treat. We have a range, such that if NNT < NNTpos, all patients will be treated, if NNT > NNTneg then all patients will not be treated. Suppose N=NNTpos is the number of patients such that if N pts are positive,one will be a true positive. The "eq" means that we choose NNTpos so that treating all or not treating all would be equivalent. E(loss | treat) = (NNTpos-1) * L[A,H] = E(loss | wait) = 1 * L[W,D] Actually we choose N SMALLER so that TREATing is definitely, comfortably the right thing. E(loss | treat) = (NNTpos-1) * L[A,H] << E(loss | wait) = 1 * L[W,D] Suppose N=NNTneg is the number of patients such that if N pts are negative, one will be a false negative. The "eq" means that we choose NNTneg so that treating all or not treating any would be equivalent. E(loss | treat) = (NNTneg-1) * L[A,H] = E(loss | wait) = 1 * L[W,D] Actually we choose N LARGER so that WAITing is definitely, comfortably the right thing. E(loss | treat) = (NNTpos-1) * L[A,H] >> E(loss | wait) = 1 * L[W,D]

Value

c(ppv=ppv, npv=npv, sp=sp, se=se)

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