Nothing
# Compute the ratio of effect sizes for proportions based on non-missing values in the combined sample
RatioEffectSizePropStat = function(sample.list, parameter) {
# Determine the function call, either to generate the statistic or to return description
call = (parameter[[1]] == "Description")
if (call == FALSE | is.na(call)) {
# Error checks
if (length(sample.list)!=4)
stop("Analysis model: Four samples must be specified in the RatioEffectSizePropStat statistic.")
# Merge the samples in the sample list
sample1 = sample.list[[1]]
# Merge the samples in the sample list
sample2 = sample.list[[2]]
# Merge the samples in the sample list
sample3 = sample.list[[3]]
# Merge the samples in the sample list
sample4 = sample.list[[4]]
# Select the outcome column and remove the missing values due to dropouts/incomplete observations
outcome1 = sample1[, "outcome"]
outcome2 = sample2[, "outcome"]
prop1 = mean(stats::na.omit(outcome1))
prop2 = mean(stats::na.omit(outcome2))
prop = (prop2 + prop1) / 2
result1 = (prop2 - prop1) / sqrt(prop * (1-prop))
# Select the outcome column and remove the missing values due to dropouts/incomplete observations
outcome3 = sample3[, "outcome"]
outcome4 = sample4[, "outcome"]
prop3 = mean(stats::na.omit(outcome3))
prop4 = mean(stats::na.omit(outcome4))
prop = (prop3 + prop4) / 2
result2 = (prop4 - prop3) / sqrt(prop * (1-prop))
# Caculate the ratio of effect size
result = result1 / result2
}
else if (call == TRUE) {
result = list("Ratio of effect size (proportion)")
}
return(result)
}
# End of RatioEffectSizePropStat
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