Unstratified and stratified Miettinen and Nurminen test in aggregate data level

Usage

rate_compare_sum(
  n0,
  n1,
  x0,
  x1,
  strata = NULL,
  delta = 0,
  weight = c("ss", "equal", "cmh"),
  test = c("one.sided", "two.sided"),
  bisection = 100,
  eps = 1e-06,
  alpha = 0.05
)

Arguments

n0, n1

The sample size in the control group and experimental group, separately. The length should be the same as the length for x0/x1 and strata.

x0, x1

The number of events in the control group and experimental group, separately. The length should be the same as the length for n0/n1 and strata.

strata

A vector of stratum indication to be used in the analysis. If NULL or the length of unique values of strata equals to 1, it is unstratified MN analysis. Otherwise, it is stratified MN analysis. The length of strata should be the same as the length for x0/x1 and n0/n1.

delta

A numeric value to set the difference of two groups under the null.

weight

Weighting schema used in stratified MN method. Default is "ss":

"equal" for equal weighting.
"ss" for sample size weighting.
"cmh" for Cochran-Mantel-Haenszel's weights.

test

A character string specifying the side of p-value, must be one of "one.sided", or "two.sided".

bisection

The number of sections in the interval used in bisection method. Default is 100.

eps

The level of precision. Default is 1e-06.

alpha

Pre-defined alpha level for two-sided confidence interval.

Value

A data frame with the test results.

References

Miettinen, O. and Nurminen, M, Comparative Analysis of Two Rates. Statistics in Medicine, 4(2):213–226, 1985.

Examples

# Conduct the stratified MN analysis with sample size weights
treatment <- c(rep("pbo", 100), rep("exp", 100))
response <- c(rep(0, 80), rep(1, 20), rep(0, 40), rep(1, 60))
stratum <- c(rep(1:4, 12), 1, 3, 3, 1, rep(1:4, 12), rep(1:4, 25))
n0 <- sapply(split(treatment[treatment == "pbo"], stratum[treatment == "pbo"]), length)
n1 <- sapply(split(treatment[treatment == "exp"], stratum[treatment == "exp"]), length)
x0 <- sapply(split(response[treatment == "pbo"], stratum[treatment == "pbo"]), sum)
x1 <- sapply(split(response[treatment == "exp"], stratum[treatment == "exp"]), sum)
strata <- c("a", "b", "c", "d")
rate_compare_sum(
  n0, n1, x0, x1,
  strata,
  delta = 0,
  weight = "ss",
  test = "one.sided",
  alpha = 0.05
)
#>         est  z_score            p     lower     upper
#> 1 0.3998397 5.712797 5.556727e-09 0.2684383 0.5172779