racusum_scores {vlad} | R Documentation |
Compute CUSUM scores based on the log-likelihood ratio statistic.
racusum_scores(wt1, wt2, reset = FALSE, h1 = NULL, h2 = NULL)
wt1 |
Double. Log-likelihood ratio scores from function |
wt2 |
Double. Log-likelihood ratio scores from function |
reset |
Logical. If |
h1 |
Double. Upper control limit of the CUSUM chart. |
h2 |
Double. Lower control limit of the CUSUM chart. |
Returns a list with two components for the CUSUM scores.
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.
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. Circulation, 79(6):I3-12.
Rigdon SE and Fricker RD (2015). Health Surveillance. In Chen DG and Wilson J (eds) Innovative Statistical Methods for Public Health Data, pp. 203–249. Springer, Cham.
## Not run: #' 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")) ## End(Not run)