powerReplicationSuccess: Computes the power for replication success with the sceptical...

In ReplicationSuccess: Design and Analysis of Replication Studies

Computes the power for replication success with the sceptical p-value

Description

Computes the power for replication success with the sceptical p-value based on the result of the original study, the corresponding variance ratio, and the design prior.

Usage

powerReplicationSuccess(
  zo,
  c = 1,
  level = 0.025,
  designPrior = c("conditional", "predictive", "EB"),
  alternative = c("one.sided", "two.sided"),
  type = c("golden", "nominal", "controlled"),
  shrinkage = 0,
  h = 0,
  strict = FALSE
)

Arguments

zo	Numeric vector of z-values from original 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.
level	Threshold for the calibrated sceptical p-value. Default is 0.025.
designPrior	Either "conditional" (default), "predictive", or "EB". If "EB", the power is computed under a predictive distribution, where the contribution of the original study is shrunken towards zero based on the evidence in the original study (with an empirical Bayes shrinkage estimator).
alternative	Specifies if level is "one.sided" (default) or "two.sided". If "one.sided" then power calculations are based on a one-sided assessment of replication success in the direction of the original effect estimates.
type	Type of recalibration. Can be either "golden" (default), "nominal" (no recalibration), or "controlled". "golden" ensures that for an original study just significant at the specified level, replication success is only possible for replication effect estimates larger than the original one. "controlled" ensures exact overall Type-I error control at level level^2.
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. Is only taken into account if the designPrior is "conditional" or "predictive".
h	Numeric vector of relative heterogeneity variances i.e., the ratios of the heterogeneity variance to the variance of the original effect estimate. Default is 0 (no heterogeneity). Is only taken into account when designPrior = "predictive" or designPrior = "EB".
strict	Logical vector indicating whether the probability for replication success in the opposite direction of the original effect estimate should also be taken into account. Default is FALSE. Only taken into account when alternative = "two.sided".

Details

powerReplicationSuccess is the vectorized version of the internal function .powerReplicationSuccess_. Vectorize is used to vectorize the function.

Value

The power for replication success with the sceptical p-value

Author(s)

Leonhard Held, Charlotte Micheloud, Samuel Pawel

References

Held, L. (2020). A new standard for the analysis and design of replication studies (with discussion). Journal of the Royal Statistical Society: Series A (Statistics in Society), 183, 431-448. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssa.12493")}

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/21-AOAS1502")}

Micheloud, C., Balabdaoui, F., Held, L. (2023). Beyond the two-trials rule: Type-I error control and sample size planning with the sceptical p-value. https://arxiv.org/abs/2207.00464

See Also

sampleSizeReplicationSuccess, pSceptical, levelSceptical

Examples

## larger sample size in replication (c > 1)
powerReplicationSuccess(zo = p2z(0.005), c = 2, level = 0.025, designPrior = "conditional")
powerReplicationSuccess(zo = p2z(0.005), c = 2, level = 0.025, designPrior = "predictive")

## smaller sample size in replication (c < 1)
powerReplicationSuccess(zo = p2z(0.005), c = 1/2, level = 0.025, designPrior = "conditional")
powerReplicationSuccess(zo = p2z(0.005), c = 1/2, level = 0.025, designPrior = "predictive")

powerReplicationSuccess(zo = p2z(0.00005), c = 2, level = 0.05,
                        alternative = "two.sided",  strict = TRUE, shrinkage = 0.9)
powerReplicationSuccess(zo = p2z(0.00005), c = 2, level = 0.05,
                        alternative = "two.sided", strict = FALSE, shrinkage = 0.9)

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