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a guest Mar 15th, 2019 51 Never
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  1. library(discreteRV)
  2.  
  3. placebo.rate <- 0.5
  4. mmm.rate <- 0.3
  5. mmm.power <- power.prop.test(p1 = placebo.rate, p2 = mmm.rate, power = 0.8, alternative = "one.sided")
  6. n <- as.integer(ceiling(mmm.power$n))
  7. patients <- seq(from = 0, to = n, by = 1)
  8. placebo_distribution <- dbinom(patients, size = n, prob = placebo.rate)
  9. mmm_distribution <- dbinom(patients, size = n, prob = mmm.rate)
  10.  
  11. get_pmf <- function(p1, p2) {
  12.   X1 <- RV(patients,p1, fractions = F)
  13.   X2 <- RV(patients,p2, fractions = F)
  14.   pmf <- joint(X1, X2, fractions = F)
  15.   return(pmf)
  16. }
  17. extract <- function(string) {
  18.   ints <- unlist(strsplit(string,","))
  19.   x1 <- as.integer(ints[1])
  20.   x2 <- as.integer(ints[2])
  21.   return(x1-x2)
  22. }
  23. diff_prob <- function(pmf) {
  24.   diff <- unname(sapply(outcomes(pmf),FUN = extract)/n)
  25.   probabilities <- unname(probs(pmf))
  26.   df <- data.frame(diff,probabilities)
  27.   df <- aggregate(. ~ diff, data = df, FUN = sum)
  28.   return(df)
  29. }
  30. most_likely_rate <- function(x) {
  31.   x[which(x$probabilities == max(x$probabilities)),]$diff
  32. }
  33.  
  34. mmm_rate_diffs <- diff_prob(get_pmf(mmm_distribution,placebo_distribution))
  35. placebo_rate_diffs <- diff_prob(get_pmf(placebo_distribution,placebo_distribution))
  36. plot(mmm_rate_diffs$diff,mmm_rate_diffs$probabilities * 100, type = "l", lty = 2, xlab = "Rate difference", ylab = "# of trials per 100", main = paste("Trials with",n,"patients per treatment arm",sep = " "))
  37. lines(placebo_rate_diffs$diff, placebo_rate_diffs$probabilities * 100, lty = 1, xaxs = "i")
  38. abline(v = c(most_likely_rate(placebo_rate_diffs), most_likely_rate(mmm_rate_diffs)), lty = c(1,2))
  39. legend("topleft", legend = c("Alternative hypothesis", "Null hypothesis"), lty = c(2,1))
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