racusum_arloc_h_sim {vlad} | R Documentation |
Compute alarm threshold (Out of Control ARL) of risk-adjusted cumulative sum control charts using simulation.
racusum_arloc_h_sim(L0, df, coeff, coeff2, R0 = 1, RA = 2, RQ = 1, m = 100, nc = 1, jmax = 4, verbose = FALSE)
L0 |
Double. Prespecified in-control Average Run Length. |
df |
Data Frame. First column are Parsonnet Score values within a range of |
coeff |
Numeric Vector. Estimated coefficients alpha and beta from the binary logistic regression model. |
coeff2 |
Numeric Vector. Estimated coefficients alpha and beta from the binary logistic regression model of a resampled dataset. |
R0 |
Double. Odds ratio of death under the null hypotheses. |
RA |
Double. Odds ratio of death under the alternative hypotheses. Detecting deterioration
in performance with increased mortality risk by doubling the odds Ratio |
RQ |
Double. Defines the performance of a surgeon with the odds ratio ratio of death |
m |
Integer. Number of simulation runs. |
nc |
Integer. Number of cores. |
jmax |
Integer. Number of digits for grid search. |
verbose |
Logical. If |
The function racusum_arloc_h_sim
determines the control limit h
for given
in-control ARL (L0
) by applying a multi-stage search procedure which includes secant
rule and the parallel version of racusum_arloc_sim
using mclapply
.
Returns a single value which is the control limit h
for a given in-control ARL.
Philipp Wittenberg
Steiner SH, Cook RJ, Farewell VT and Treasure T (2000). Monitoring surgical performance using risk-adjusted cumulative sum charts. Biostatistics, 1(4), pp. 441–452.
Wittenberg P, Gan FF, Knoth S (2018). A simple signaling rule for variable life-adjusted display derived from an equivalent risk-adjusted CUSUM chart. Statistics in Medicine, 37(16), pp 2455–2473.
## Not run: library(vlad) # Set seed for reproducibility RNGkind("L'Ecuyer-CMRG") set.seed(1234) parallel::mc.reset.stream() # Datasets data("cardiacsurgery", package = "spcadjust") 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 # Deterioration: # 1. Determine critical value for given ARL racusum_arloc_h_sim(L0 = 370, df = df1, coeff = coeff1, coeff2 = coeff2, m = m, RA = 2, nc = 6) # h = 2.030933 # 2. Determine ARL and Standard Error RLS <- do.call(c, parallel::mclapply(1:m, racusum_arloc_sim, h = 2.035, df = df1, RA = 2, coeff = coeff1, coeff2 = coeff2, mc.cores = 6)) data.frame(cbind("ARL"=mean(RLS), "ARLSE"=sd(RLS)/sqrt(m))) # ARL = 371.125; ARLSE = 11.36053 # Improvement: # 1. Determine critical value for given ARL racusum_arloc_h_sim(L0 = 370, df = df1, coeff = coeff1, coeff2 = coeff2, m = m, RA = 1/2, nc = 6) # h = 1.710999 # # 2. Determine ARL and Standard Error RLS <- do.call(c, parallel::mclapply(1:m, racusum_arloc_sim, h = 1.760, df = df1, RA = 1/2, coeff = coeff1, coeff2 = coeff2, mc.cores = 6)) data.frame(cbind("ARL" = mean(RLS), "ARLSE" = sd(RLS)/sqrt(m))) # ARL = 399.613; ARLSE = 10.7601 ## End(Not run)