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######################################################################################################################
# Function: GLMNegBinomTest.
# Argument: Data set and parameter (call type).
# Description: Computes one-sided p-value based on Negative-Binomial regression.
GLMNegBinomTest = function(sample.list, parameter) {
# Determine the function call, either to generate the p-value or to return description
call = (parameter[[1]] == "Description")
if (call == FALSE | is.na(call)) {
# No parameters are defined
if (is.na(parameter[[2]])) {
larger = TRUE
}
else {
if (!all(names(parameter[[2]]) %in% c("larger"))) stop("Analysis model: GLMNegBinomTest test: this function accepts only one argument (larger)")
# Parameters are defined but not the larger argument
if (!is.logical(parameter[[2]]$larger)) stop("Analysis model: GLMNegBinomTest test: the larger argument must be logical (TRUE or FALSE).")
larger = parameter[[2]]$larger
}
# Sample list is assumed to include two data frames that represent two analysis samples
# Outcomes in Sample 1
outcome1 = sample.list[[1]][, "outcome"]
# Remove the missing values due to dropouts/incomplete observations
outcome1.complete = outcome1[stats::complete.cases(outcome1)]
# Outcomes in Sample 2
outcome2 = sample.list[[2]][, "outcome"]
# Remove the missing values due to dropouts/incomplete observations
outcome2.complete = outcome2[stats::complete.cases(outcome2)]
# Data frame
data.complete = data.frame(rbind(cbind(2, outcome2.complete), cbind(1, outcome1.complete)))
colnames(data.complete) = c("TRT", "RESPONSE")
data.complete$TRT=as.factor(data.complete$TRT)
# One-sided p-value (to be checked)
# result = summary(MASS::glm.nb(RESPONSE ~ TRT, data = data.complete))$coefficients["TRT2", "Pr(>|z|)"]/2
z = summary(MASS::glm.nb(RESPONSE ~ TRT, data = data.complete))$coefficients["TRT2", "z value"]
result = stats::pnorm(z, lower.tail = !larger)
}
else if (call == TRUE) {
result=list("Negative-binomial regression test")
}
return(result)
}
# End of GLMNegBinomTest
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