# Unstratified and stratified Miettinen and Nurminen test in aggregate data level

Source:`R/rate_compare.R`

`rate_compare_sum.Rd`

Unstratified and stratified Miettinen and Nurminen test in aggregate data level

## 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.

## 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
```