bdpv (version 1.3)

simPVmat: Simulate performance of confidence intervals for predictive values in case-control design

Description

Simulate the power (probability to exclude NPV0/PPV0), the coverage probability, and 0.1, 0.2, and 0.5-quantiles of the distribution of (lower!) asymptotic confidence bounds for predictive values. Different experimental setups may be compared. The function draws data under the binomial assumption and computes the asymptotic confidence bounds (lower bounds only!) for the positive and negative predictive values.

Usage

simPVmat(se, sp, pr, n1, n0, NPV0, PPV0,
 conf.level = 0.95, NSIM = 500, setnames = NULL)

Arguments

se

a (vector of) numeric value(s), specifying sensitivity

sp

a (vector of) numeric value(s), specifying specitivity

pr

a (vector of) numeric value(s), specifying prevalence

n1

a (vector of integer) value(s), specifying the number of truely positive compounds in the trial

n0

a (vector of integer) value(s), specifying the number of truely negative compounds in the trial

NPV0

a (vector of) numeric value(s), specifying the hypothesized negative predictive value (NPV assumed under H0)

PPV0

a (vector of) numeric value(s), specifying the hypothesized positive predictive value (PPV assumed under H0)

conf.level

a single numeric value, the confidence level

NSIM

a single (integer) value, the number of simulations to be run

setnames

optional character vector to the parameter sets in the output

Value

A list with elements

INDAT

a dataframe with rows showing the sets of parameters build from the input values and columns: se, sp, pr, NPV0, PPV0, n1, n0, n (total sample size)

NPV

a matrix with simulation results for the negative predictive value

PPV

a matrix with simulation results for the positive predictive value

NSIM

number of suimulations

conf.level

nominal confidence level

Details

The vector or single values in se, sp, pr, n1, n0, NPV0, PPV0 are put together (shorter vectors recycled to the length of longest vectors). Then each of the resulting parameter settings is simulated as described in simPV

See Also

This function is meantb to check small sample results obtained by the asymptotci formulas for experimental design from nPV, nNPV, nPPV

Examples

# NOT RUN {
simPVmat(se=0.8, sp=0.95, pr=1/16,
 n1=c(177, 181), n0=c(554, 87), NPV0=0.98, PPV0=c(0.4, 0.25))


simPVmat(se=0.8, sp=0.95, pr=c(0.05,0.0625, 0.075, 0.1),
 n1=177, n0=554, NPV0=0.98, PPV0=0.4)



# }