porsTOST: Probability of replication success based on TOST equivalence

In BayesRepDesign: Bayesian Design of Replication Studies

Probability of replication success based on TOST equivalence

Description

This function computes the probability to achieve replication success based on establishing the absence of a practically relevant effect size with the Two One-Sided Tests (TOST) procedure in the replication study.

Usage

porsTOST(level, dprior, margin, sr)

Arguments

level	Significance level for the TOST p-value
dprior	Design prior object
margin	The equivalence margin > 0 for the equivalence region around zero that defines a region of practically irrelevant effect sizes
sr	Replication standard error

Value

The probability to achieve replication success

Author(s)

Samuel Pawel

References

Pawel, S., Consonni, G., and Held, L. (2022). Bayesian approaches to designing replication studies. arXiv preprint. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2211.02552")}

Anderson, S. F. and Maxwell, S. E. (2016). There's more than one way to conduct a replication study: Beyond statistical significance. Psychological Methods, 21(1), 1-12. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000051")}

Examples

## specify design prior
to1 <- 2
so1 <- 0.05
dprior <- designPrior(to = to1, so = so1, tau = 0.1)
porsTOST(level = 0.1, dprior = dprior, margin = 0.3, sr = c(0.05, 0.03))

BayesRepDesign documentation built on May 4, 2023, 1:07 a.m.