PPpSceptical {ReplicationSuccess}

Compute project power of the sceptical p-value

Description

The project power of the sceptical p-value is computed for a specified level, the relative variance, significance level and power for a standard significance test of the original study, and the alternative hypothesis.

Usage

PPpSceptical(
  level,
  c,
  alpha,
  power,
  alternative = c("one.sided", "two.sided"),
  type = c("golden", "nominal", "controlled")
)

Arguments

level

Threshold for the calibrated sceptical p-value. Default is 0.025.

c

Numeric vector of variance ratios of the original and replication effect estimates. This is usually the ratio of the sample size of the replication study to the sample size of the original study.

alpha

Significance level for a standard significance test in the original study. Default is 0.025.

power

Power to detect the assumed effect with a standard significance test in the original study.

alternative

Specifies if level and alpha are "two.sided" or "one.sided".

type

Type of recalibration. Can be either "golden" (default), "nominal" (no recalibration), or "controlled".

Details

PPpSceptical is the vectorized version of the internal function .PPpSceptical_. Vectorize is used to vectorize the function.

Value

The project power of the sceptical p-value

Author(s)

Leonhard Held, Samuel Pawel

References

Held, L. (2020). The harmonic mean chi-squared test to substantiate scientific findings. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69, 697-708. doi:10.1111/rssc.12410

Held, L., Micheloud, C., Pawel, S. (2022). The assessment of replication success based on relative effect size. The Annals of Applied Statistics. 16:706-720.doi:10.1214/21-AOAS1502

Maca, J., Gallo, P., Branson, M., and Maurer, W. (2002). Reconsidering some aspects of the two-trials paradigm. Journal of Biopharmaceutical Statistics, 12, 107-119. doi:10.1081/bip-120006450

See Also

pSceptical, levelSceptical, T1EpSceptical

Examples

## compare project power for different recalibration types
types <- c("nominal", "golden", "controlled")
c <- seq(0.4, 5, by = 0.01)
alpha <- 0.025
power <- 0.9
pp <- sapply(X = types, FUN = function(t) {
  PPpSceptical(type = t, c = c, alpha, power, alternative = "one.sided",
               level = 0.025)
})

## compute project power of 2 trials rule
za <- qnorm(p = 1 - alpha)
mu <- za + qnorm(p = power)
pp2TR <- power * pnorm(q = za, mean = sqrt(c) * mu, lower.tail = FALSE)

matplot(x = c, y = pp * 100, type = "l", lty = 1, lwd = 2, las = 1, log = "x",
        xlab = bquote(italic(c)), ylab = "Project power (%)", xlim = c(0.4, 5),
        ylim = c(0, 100))
lines(x = c, y = pp2TR * 100, col = length(types) + 1, lwd = 2)
abline(v = 1, lty = 2)
abline(h = 90, lty = 2, col = "lightgrey")
legend("bottomright", legend = c(types, "2TR"), lty = 1, lwd = 2,
       col = seq(1, length(types) + 1))