powerSignificanceInterim {ReplicationSuccess} | R Documentation |
Computes the power of a replication study taking into account data from an interim analysis.
powerSignificanceInterim(zo, zi, c = 1, f = 1/2, level = 0.025, designPrior = "conditional", analysisPrior = "flat", alternative="greater", shrinkage = 0)
zo |
A vector of z-values from original studies. |
zi |
A vector of z-values from interim analyses of replication studies. |
c |
Ratio of the sample size of the replication study to the sample size of the original study. |
f |
Fraction of the replication study already completed. |
level |
The significance level. |
designPrior |
Either |
analysisPrior |
Either |
alternative |
Either |
shrinkage |
A number in [0,1].
Defaults to |
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.
Charlotte Micheloud
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. (2020). Power Calculations for Replication Studies. https://arxiv.org/abs/2004.10814
sampleSizeSignificance
, powerSignificance
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)