racusum_crit_sim {vlad} | R Documentation |
Compute alarm threshold of risk-adjusted cumulative sum control charts using simulation.
racusum_crit_sim(L0, df, coeff, R0 = 1, RA = 2, m = 100, yemp = TRUE, 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. |
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 |
m |
Integer. Number of simulation runs. |
yemp |
Logical. If |
nc |
Integer. Number of cores used for parallel processing. |
jmax |
Integer. Number of digits for grid search. |
verbose |
Logical. If |
The function racusum_crit_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_arl_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) library("dplyr") data("cardiacsurgery", package = "spcadjust") ## preprocess data to 30 day mortality and subset phase I (In-control) of surgeons 2 S2I <- cardiacsurgery %>% rename(s = Parsonnet) %>% mutate(y = ifelse(status == 1 & time <= 30, 1, 0), phase = factor(ifelse(date < 2*365, "I", "II"))) %>% filter(phase == "I", surgeon == 2) %>% select(s, y) ## estimate coefficients from logit model coeff1 <- round(coef(glm(y ~ s, data = S2I, family = "binomial")), 3) ## control limit for detecting deterioration RA = 2: racusum_crit_sim(L0 = 740, df = S2I, coeff = coeff1, m = 10^3, nc = 4) ## End(Not run)