## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

## ----setup--------------------------------------------------------------------
library(respondeR)

## -----------------------------------------------------------------------------
sample_responder_data

## -----------------------------------------------------------------------------
res <- responder_analysis(sample_responder_data, mid = 1)
res[, c("method", "p_e", "p_c", "rd", "rd_lb", "rd_ub", "rr", "or", "nnt")]

## -----------------------------------------------------------------------------
format_responder_results(res)

## -----------------------------------------------------------------------------
responder_analysis(sample_responder_data, mid = 1, direction = "lower",
                   method = "individual")[, c("method", "rd", "rd_lb", "rd_ub")]

## -----------------------------------------------------------------------------
responder_analysis(sample_responder_data, mid = 1, control = "median")[,
  c("method", "p_e", "p_c", "rd")]

## -----------------------------------------------------------------------------
responder_rd_individual(sample_responder_data, mid = 1)

## ----fig.width = 6, fig.height = 3.2, fig.alt = "Forest plot of per-study responder risk differences"----
ps <- responder_rd_individual(sample_responder_data, mid = 1)
pooled <- responder_analysis(sample_responder_data, mid = 1, method = "individual")

y <- rev(seq_len(nrow(ps) + 1))
est <- c(ps$rd, pooled$rd) * 100
lo  <- c(ps$ci_lb, pooled$rd_lb) * 100
hi  <- c(ps$ci_ub, pooled$rd_ub) * 100
labels <- c(as.character(ps$study), "Pooled")

op <- par(mar = c(4, 6, 1, 1))
plot(NA, xlim = range(c(lo, hi, 0)), ylim = c(0.5, length(y) + 0.5),
     yaxt = "n", xlab = "Risk difference (%)", ylab = "", bty = "n")
abline(v = 0, lty = 2, col = "grey60")
segments(lo, y, hi, y, lwd = 2)
points(est, y, pch = c(rep(15, nrow(ps)), 18), cex = c(rep(1.4, nrow(ps)), 2))
axis(2, at = y, labels = labels, las = 1, tick = FALSE)
par(op)

## -----------------------------------------------------------------------------
responder_analysis(sample_responder_data, mid = 1, method = "individual",
                   pooling = "random")[, c("method", "rd", "rd_lb", "rd_ub",
                                           "tau2", "i2", "pi_lb", "pi_ub")]

## -----------------------------------------------------------------------------
cles <- responder_cles(sample_responder_data)
sprintf("CLES = %.1f%% (%.1f%% to %.1f%%)",
        100 * cles$cles, 100 * cles$cles_lb, 100 * cles$cles_ub)

## -----------------------------------------------------------------------------
res <- responder_analysis(vas_pain, mid = -1.5, direction = "lower",
                          pooling = "random", ci_method = "hksj")
format_responder_results(res)

## -----------------------------------------------------------------------------
cles <- responder_cles(vas_pain, direction = "lower")
sprintf("A treated patient has less pain than a control %.0f%% of the time (%.0f%% to %.0f%%)",
        100 * cles$cles, 100 * cles$cles_lb, 100 * cles$cles_ub)

## ----eval = FALSE-------------------------------------------------------------
# launch_responder_analysis()