Nothing
######################################################################################################################
# Function: TTestNI .
# Argument: Data set and parameter (call type and non-inferiority margin).
# Description: Computes one-sided p-value based on two-sample t-test with a non-inferiority margin.
TTestNI = 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)) {
if (is.null(parameter[[2]]$margin))
stop("Analysis model: TTestNI test: Non-inferiority margin must be specified.")
margin = as.numeric(parameter[[2]]$margin)
# Check if larger treatment effect is expected for the second sample or not (default = TRUE)
if (is.null(parameter[[2]]$larger)) larger = TRUE
else {
if (!is.logical(parameter[[2]]$larger))
stop("Analysis model: TTestNI test: the larger argument must be logical (TRUE or FALSE).")
larger = parameter[[2]]$larger
}
# Sample list is assumed to include two data frames that represent two analysis samples
# Outcomes in Sample 1
outcome1 = sample.list[[1]][, "outcome"]
# Remove the missing values due to dropouts/incomplete observations
outcome1.complete = outcome1[stats::complete.cases(outcome1)]
# Outcomes in Sample 2
outcome2 = sample.list[[2]][, "outcome"]
# Remove the missing values due to dropouts/incomplete observations
outcome2.complete = outcome2[stats::complete.cases(outcome2)]
# One-sided p-value (treatment effect in sample 2 is expected to be greater than in sample 1)
if (larger) result = stats::t.test(outcome2.complete + margin, outcome1.complete, alternative = "greater")$p.value
else result = stats::t.test(outcome1.complete + margin, outcome2.complete, alternative = "greater")$p.value
}
else if (call == TRUE) {
result=list("Student's t-test (non-inferiority)")
}
return(result)
}
# End of TTestNI
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.