CrossScreening (version 0.1.1)

power.sen: Power of sensitivity analysis

Description

Power of sensitivity analysis

Usage

power.sen(mu.F = 1/2, sigma.F = sqrt(1/3), d = NULL, mm = c(2, 2, 2),
  gamma = 1, alpha = 0.05, I = 100, approx.method = c("changing.alpha",
  "fixed.alpha"), score.method = c("approximate", "exact"))

Arguments

mu.F

mean of the signed rank statistic

sigma.F

standard deviation of the signed rank statistic

d

empirical data used to estimate mu.F and sigma.F by jackknife

mm

test statistic, either a vector of length 3 or a matrix of three rows where each column corresponds to a U-statistic. Default is the (approximate) Wilcoxon's signed rank test.

gamma

target sensitivity level

alpha

target significance level

I

sample size

approx.method

which approximation method to use?

score.method

either approximate score or exact score

Value

power of the sensitivity analysis, possibly a vector if mm has multiple columns.

Details

If approx.method is "fixed.alpha", then the significance level alpha is considered fixed and the corresponding quantile negligible. Otherwise we also use the alpha-quantile in the approximation formula. For more detail, see the reference.

References

Qingyuan Zhao. On sensitivity value of pair-matched observational studies. arXiv 1702.03442, https://arxiv.org/abs/1702.03442.

Examples

power.sen(d = rnorm(100) + 0.5, I = 200, gamma = 2)

## The following code reproduces an example of power analysis in Zhao (2017)
power.sen(0.76, sqrt(0.26), gamma = 2.5, I = 200)
power.sen(0.76, sqrt(0.26), gamma = 2.5, I = 200, approx.method = "fixed.alpha")