calc_K_disj_2dftest {crt2power}

Calculate required number of clusters per treatment group for a cluster-randomized trial with co-primary endpoints using a disjunctive 2-DF test approach.

Description

Allows user to calculate the number of clusters per treatment arm of a cluster-randomized trial with two co-primary outcomes given a set of study design input values, including the statistical power, and cluster size. Uses the disjunctive 2-DF test approach. Code is adapted from "calSampleSize_omnibus()" from https://github.com/siyunyang/coprimary_CRT.

Usage

calc_K_disj_2dftest(
  dist = "Chi2",
  power,
  m,
  alpha = 0.05,
  beta1,
  beta2,
  varY1,
  varY2,
  rho01,
  rho02,
  rho1,
  rho2,
  r = 1
)

Arguments

dist

Specification of which distribution to base calculation on, either 'Chi2' for Chi-Squared or 'F' for F-Distribution.

power

Desired statistical power in decimal form; numeric.

m

Individuals per cluster; numeric.

alpha

Type I error rate; numeric.

beta1

Effect size for the first outcome; numeric.

beta2

Effect size for the second outcome; numeric.

varY1

Total variance for the first outcome; numeric.

varY2

Total variance for the second outcome; numeric.

rho01

Correlation of the first outcome for two different individuals in the same cluster; numeric.

rho02

Correlation of the second outcome for two different individuals in the same cluster; numeric.

rho1

Correlation between the first and second outcomes for two individuals in the same cluster; numeric.

rho2

Correlation between the first and second outcomes for the same individual; numeric.

r

Treatment allocation ratio - K2 = rK1 where K1 is number of clusters in experimental group; numeric.

Value

A data frame of numerical values.

Examples

calc_K_disj_2dftest(power = 0.8, m = 300, alpha = 0.05,
beta1 = 0.1, beta2 = 0.1, varY1 = 0.23, varY2 = 0.25,
rho01 = 0.025, rho02 = 0.025, rho1 = 0.01, rho2  = 0.05)