powerSignificanceInterim: Interim power of a replication study

In ReplicationSuccess: Design and Analysis of Replication Studies

Interim power of a replication study

Description

Computes the power of a replication study taking into account data from an interim analysis.

Usage

powerSignificanceInterim(
  zo,
  zi,
  c = 1,
  f = 1/2,
  level = 0.025,
  designPrior = c("conditional", "informed predictive", "predictive"),
  analysisPrior = c("flat", "original"),
  alternative = c("one.sided", "two.sided"),
  shrinkage = 0
)

Arguments

zo	Numeric vector of z-values from original studies.
zi	Numeric vector of z-values from interim analyses of replication studies.
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. Default is 1.
f	Fraction of the replication study already completed. Default is 0.5.
level	Significance level. Default is 0.025.
designPrior	Either "conditional" (default), "informed predictive", or "predictive". "informed predictive" refers to an informative normal prior coming from the original study. "predictive" refers to a flat prior.
analysisPrior	Either "flat" (default) or "original".
alternative	Either "one.sided" (default) or "two.sided". Specifies if the significance level is one-sided or two-sided.
shrinkage	Numeric vector with values in [0,1). Defaults to 0. Specifies the shrinkage of the original effect estimate towards zero, e.g., the effect is shrunken by a factor of 25% for shrinkage=0.25.

Details

This is an extension of powerSignificance() and adapts the ‘interim power’ from section 6.6.3 of Spiegelhalter et al. (2004) to the setting of replication studies.

powerSignificanceInterim is the vectorized version of .powerSignificanceInterim_. Vectorize is used to vectorize the function.

Value

The probability of statistical significance in the specified direction at the end of the replication study given the data collected so far in the replication study.

Author(s)

Charlotte Micheloud

References

Spiegelhalter, D. J., Abrams, K. R., and Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation, volume 13. John Wiley & Sons

Micheloud, C., Held, L. (2022). Power Calculations for Replication Studies. Statistical Science, 37, 369-379. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/21-STS828")}

See Also

sampleSizeSignificance, powerSignificance

Examples

powerSignificanceInterim(zo = 2, zi = 2, c = 1, f = 1/2,
                         designPrior = "conditional",
                         analysisPrior = "flat")

powerSignificanceInterim(zo = 2, zi = 2, c = 1, f = 1/2,
                         designPrior = "informed predictive",
                         analysisPrior = "flat")

powerSignificanceInterim(zo = 2, zi = 2, c = 1, f = 1/2,
                         designPrior = "predictive",
                         analysisPrior = "flat")

powerSignificanceInterim(zo = 2, zi = -2, c = 1, f = 1/2,
                         designPrior = "conditional",
                         analysisPrior = "flat")

powerSignificanceInterim(zo = 2, zi = 2, c = 1, f = 1/2,
                         designPrior = "conditional",
                         analysisPrior = "flat",
                         shrinkage = 0.25)

ReplicationSuccess documentation built on April 3, 2023, 5:11 p.m.