vlad (version 0.2.2)

racusum_discretebeta_crit_sim: Compute alarm threshold of RA-CUSUM control charts using simulation

Description

Compute alarm threshold of risk-adjusted cumulative sum control charts using simulation.

Usage

racusum_discretebeta_crit_sim(L0, shape1, shape2, coeff, rs = 72, RA = 2,
  RQ = 1, nc = 1, hmax = 30, jmax = 4, m = 10000, verbose = FALSE)

Arguments

L0

Double. Prespecified in-control Average Run Length.

shape1

Double. Shape parameter \(\alpha\) > 0 of the beta distribution.

shape2

Double. Shape parameter \(\beta\) > 0 of the beta distribution.

coeff

Numeric Vector. Estimated intercept and slope coefficients from a binary logistic regression model.

rs

Integer. Number of intervals between 0 and the maximum risk score.

RA

Double. Odds ratio of death under the alternative hypotheses. Detecting deterioration in performance with increased mortality risk by doubling the odds Ratio RA = 2. Detecting improvement in performance with decreased mortality risk by halving the odds ratio of death RA = 1/2.

RQ

Double. Defines the performance of a surgeon with the odds ratio ratio of death. Q.

nc

Integer. Number of cores used for parallel processing. Value is passed to parSapply.

hmax

Integer. Maximum value of h for the grid search.

jmax

Integer. Number of digits for grid search.

m

Integer. Number of simulation runs.

verbose

Logical. If TRUE verbose output is included, if FALSE a quiet calculation of h is done.

Value

Returns a single value which is the control limit h for a given in-control ARL.

Details

Determines the control limit ("h") for given in-control ARL ("L0") applying a grid search using racusum_discretebeta_arl_sim and parSapply.

Examples

# NOT RUN {
library(vlad)
racusum_discretebeta_crit_sim(L0=7500, shape1=.61, shape2=4.09, rs=(71+1),
coeff=c(-3.6798, .0768), RA=2, RQ=1, nc=4, verbose=TRUE, m=1e3)
# }
# NOT RUN {
# }