man-roxygen/racusum_scores.R

In wittenberg/vlad: Variable Life Adjusted Display and Other Risk-Adjusted Quality Control Charts

#' @references Steiner SH, Cook RJ, Farewell VT and Treasure T (2000).
#'  Monitoring surgical performance using risk-adjusted cumulative sum charts.
#'  \emph{Biostatistics}, \strong{1}(4), pp. 441-452.
#'
#' Parsonnet V, Dean D, Bernstein AD (1989). A method of uniform stratification of risk
#' for evaluating the results of surgery in acquired adult heart disease.
#' \emph{Circulation}, \strong{79}(6):I3-12.
#'
#' Rigdon SE and Fricker RD (2015). Health Surveillance.
#' In Chen DG and Wilson J (eds) \emph{Innovative Statistical Methods for Public Health Data},
#' pp. 203--249. Springer, Cham.
#'
#' @examples
#' \dontrun{
#' # library(vlad)
#' # patient Cusum values with different odds ratios, see Rigdon and Fricker p. 225, 226
#' coeff <- c("(Intercept)" = -3.67, "Parsonnet" = 0.077)
#' wt1 <- round(llr_score(df = data.frame(19L, 0), coeff = coeff, R0 = 1, RA = 2), 4)
#' wt2 <- round(llr_score(df = data.frame(19L, 0), coeff = coeff, R0 = 1, RA = 1/2), 5)
#' all.equal(racusum_scores(wt1 = wt1, wt2 = wt2), list(s1 = 0, s1l = 0.05083))
#'
#' library("dplyr")
#' library("tidyr")
#' library(ggplot2)
#' data("cardiacsurgery", package = "spcadjust")
#'
#' ## preprocess data to 30 day mortality and subset phase I (In-control)
#' SALL <- cardiacsurgery %>% rename(s = Parsonnet) %>%
#'   mutate(y = ifelse(status == 1 & time <= 30, 1, 0),
#'          phase = factor(ifelse(date < 2*365, "I", "II")))
#'
#' ## subset phase I (In-control)
#' SI <- filter(SALL, phase == "I") %>% select(s, y)
#'
#' ## estimate coefficients from logit model
#' coeff1 <- round(coef(glm(y ~ s, data = SI, family = "binomial")), 3)
#'
#' ## subset phase II of surgeons 2
#' S2II <- filter(SALL, phase == "II", surgeon == 2) %>% select(s, y)
#' n <- nrow(S2II)
#'
#' ## CUSUM statistic without reset
#' wt1 <- sapply(1:n, function(i) llr_score(S2II[i, c("s", "y")], coeff = coeff, RA = 2))
#' wt2 <- sapply(1:n, function(i) llr_score(S2II[i, c("s", "y")], coeff = coeff, RA = 1/2))
#' cv <- racusum_scores(wt1 = wt1, wt2 = wt2)
#' s1 <- cv$s1; s1l <- cv$s1l
#' dm1 <- data.frame(cbind("n" = 1:length(s1), "Cup" = s1, "Clow" = -s1l, "h1" = 2, "h2" = -2))
#'
#' ## CUSUM statistic reset after signal
#' cv <- racusum_scores(wt1 = wt1, wt2 = wt2, reset = TRUE, h1 = 2, h2 = 2)
#' s1 <- cv$s1; s1l <- cv$s1l
#' dm2 <- data.frame(cbind("n" = 1:length(s1), "Cup" = s1, "Clow" = -s1l, "h1" = 2, "h2" = -2))
#'
#' ## plot
#' dm3 <- bind_rows(dm1, dm2, .id = "type")
#' dm3$type <- recode_factor(dm3$type, `1`="No resetting", `2`="Resetting")
#' dm3 %>%
#'   gather("CUSUM", value, c(-n, - type)) %>%
#'   ggplot(aes(x = n, y = value, colour = CUSUM, group = CUSUM)) +
#'   geom_hline(yintercept = 0, colour = "darkgreen", linetype = "dashed") +
#'   geom_line(size = 0.5) +
#'   facet_wrap( ~ type, ncol = 1, scales = "free") +
#'   labs(x = "Patient number n", y = "CUSUM values") + theme_classic() +
#'   scale_y_continuous(sec.axis = dup_axis(name = NULL, labels = NULL)) +
#'   scale_x_continuous(sec.axis = dup_axis(name = NULL, labels = NULL)) +
#'   guides(colour = "none") +
#'   scale_color_manual(values = c("blue", "orange", "red", "red"))
#' }