vlad (version 0.1.0)

eocusum_arloc_h_sim: Compute alarm threshold of Out of Control EO-CUSUM control charts using simulation

Description

Compute alarm threshold (Out of Control ARL) of EO-CUSUM control charts using simulation.

Usage

eocusum_arloc_h_sim(L0, k, df, coeff, coeff2, m = 100, QS = 1,
  side = "low", nc = 1, verbose = FALSE)

Arguments

L0

double. Prespecified in-control Average Run Length.

k

double. Reference value of the CUSUM control chart.

df

DataFrame. First column are Parsonnet Score values within a range of zero to 100 representing the preoperative patient risk. The second column are binary (0/1) outcome values of each operation.

coeff

NumericVector. Estimated coefficients \(\alpha\) and \(\beta\) from the binary logistic regression model.

coeff2

NumericVector. Estimated coefficients \(\alpha\) and \(\beta\) from the binary logistic regression model of a resampled dataset.

m

integer. Number of simulation runs.

QS

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

side

character. Default is "low" to calculate ARL for the upper arm of the V-mask. If side = "up", calculate the lower arm of the V-mask.

nc

integer. Number of cores.

verbose

boolean. 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 ARL.

Details

The function eocusum_arloc_h_sim determines the control limit for given in-control ARL (L0) by applying a multi-stage search procedure which includes secant rule and the parallel version of eocusum_arloc_sim using mclapply.

Examples

# NOT RUN {
library("vlad")
library("spcadjust")
## Datasets
data("cardiacsurgery")
s5000 <- dplyr::sample_n(cardiacsurgery, size=5000, replace=TRUE)
df1 <- subset(cardiacsurgery, select=c(Parsonnet, status))
df2 <- subset(s5000, select=c(Parsonnet, status))
## estimate coefficients from logit model
coeff1 <- round(coef(glm(status~Parsonnet, data=df1, family="binomial")), 3)
coeff2 <- round(coef(glm(status~Parsonnet, data=df2, family="binomial")), 3)
## Number of simulation runs
m <- 10^3
## Number of cores
nc <- parallel::detectCores()
## Lower cusum (detecting deterioration)
## k = 0
eocusum_arloc_h_sim(L0=370, df=df1, k=0, m=m, side="low", coeff=coeff1, coeff2=coeff2, nc=nc)
## k = kopt
QA <- 2
# use package function optimal_k to determine k
kopt <- optimal_k(QA=QA, parsonnetscores=df1$Parsonnet, coeff=coeff1)
eocusum_arloc_h_sim(L0=370, df=df1, k=kopt, m=m, side="low", coeff=coeff1, coeff2=coeff2, nc=nc)
## Upper cusum (detecting improvement)
## k = 0
eocusum_arloc_h_sim(L0=370, df=df1, k=0, m=m, side="up", coeff=coeff1, coeff2=coeff2, nc=nc)
## k = kopt
QA <- 1/2
kopt <- optimal_k(QA=QA, parsonnetscores=df1$Parsonnet, coeff=coeff1)
eocusum_arloc_h_sim(L0=370, df=df1, k=kopt, m=m, side="up", coeff=coeff1, coeff2=coeff2, nc=nc)
# }