multiCA (version 1.2.0)

power.CA.test: Power calculations for the Cochran-Armitage trend test

Description

Power calculations for the Cochran-Armitage trend test

Usage

power.CA.test(
  N = NULL,
  power = NULL,
  pvec = NULL,
  scores = seq_along(pvec),
  n.prop = rep(1, length(pvec)),
  sig.level = 0.05,
  alternative = c("two.sided", "less", "greater")
)

Value

object of class "power.htest"

Arguments

N

integer, the total sample size of the study. If NULL then power needs to be specified.

power

target power. If NULL then N needs to be specified.

pvec

numeric vector of hypothesized outcome probabilities in each group.

scores

non-decreasing numeric vector of the same length as the number of ordered groups giving the trend test scores. Defaults to linearly increasing values.

n.prop

numeric vector describing relative sample sizes of the ordered groups. Will be normalized to sum to 1. Defaults to equal sample sizes.

sig.level

significance level

alternative

character string specifying the alternative hypothesis

References

Nam, J. (1987). A Simple Approximation for Calculating Sample Sizes for Detecting Linear Trend in Proportions. Biometrics, 43(3), 701-705.

Examples

# sample size required to detect with 80% power a decreasing trend over 4 groups 
# with 3:2:1:2 sample-size distribution at a 2.5% significance level
power.CA.test(power=0.8, pvec=c(0.4, 0.3, 0.2, 0.1), n.prop=c(3,2,1,2),
              alternative = "less", sig.level=0.025)

# power of a 2-sided test to detect a logistic increase with slope 0.2 over 5 groups
# with groups of size 10 with unequal dose spacing
doses <- c(0,1,2,4,8)
p0 <- 0.05 # event probability at lowest dose
logit.props <- log(p0/(1-p0)) + doses * 0.2
p <- 1 / (1 + exp(-logit.props)) # hypothesized probabilities at each dose
power.CA.test(N = 10 * 5, pvec=p, scores = doses)