Compute alarm threshold of risk-adjusted cumulative sum control charts using simulation.
racusum_arl_h_sim(L0, df, coeff, R0 = 1, RA = 2, m = 100,
yemp = TRUE, nc = 1, jmax = 4, verbose = FALSE)
Double. Prespecified in-control Average Run Length.
Data Frame. First column are Parsonnet Score values within a range of 0
to
100
representing the preoperative patient risk. The second column are binary (0/1
)
outcome values of each operation.
Numeric Vector. Estimated coefficients \(\alpha\) and \(\beta\) from the binary logistic regression model.
Double. Odds ratio of death under the null hypotheses.
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
.
Integer. Number of simulation runs.
Logical. If TRUE
, use emirical outcome values, else use model.
Integer. Number of cores used for parallel processing.
Integer. Number of digits for grid search.
Logical. If TRUE
verbose output is included, if FALSE
a quiet
calculation of h
is done.
Returns a single value which is the control limit h
for a given in-control ARL.
The function racusum_arl_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_arl_sim
using mclapply
.
# NOT RUN {
# This function is deprecated. See racusum_crit_sim() instead.
# }
Run the code above in your browser using DataLab