sampleSizeSignificance {ReplicationSuccess} | R Documentation |
The relative sample size to achieve significance is computed based on the z-value of the original study, the power, the design prior, and the relative heterogeneity.
sampleSizeSignificance(zo, power, level = 0.025, designPrior = "conditional", alternative = "one.sided", d = 0, shrinkage = 0)
zo |
A vector of z-values from original studies. |
power |
The power to achieve replication success. |
level |
Significance level. Default is 0.025. |
designPrior |
Either |
alternative |
Either |
d |
The relative between-study heterogeneity, i.e. the ratio of the heterogeneity variance to the variance of the original effect estimate.
Default is |
shrinkage |
A number in [0,1].
Defaults to |
The relative sample size to achieve significance in the specified direction. If larger than 1000 then NA is returned.
Leonhard Held, Samuel Pawel
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. https://doi.org/10.1111/rssa.12493
Pawel, S., Held, L. (2020). Probabilistic forecasting of replication studies. PLoS ONE 15(4):e0231416. https://doi.org/10.1371/journal.pone.0231416
sampleSizeSignificance(zo = p2z(0.005), power = 0.8) sampleSizeSignificance(zo = p2z(0.005, alternative = "greater"), power = 0.8) sampleSizeSignificance(zo = p2z(0.005), power = 0.8, designPrior = "predictive") sampleSizeSignificance(zo = 3, power = 0.8, designPrior = "predictive", shrinkage = 0.5, d = 0.25) sampleSizeSignificance(zo = 3, power = 0.8, designPrior = "EB", d = 0.5) # required relative sample size for 0.8 power as function of original p-value zo <- p2z(seq(0.0001, 0.05, 0.0001)) plot(z2p(zo), sampleSizeSignificance(zo = zo, designPrior = "conditional", power = 0.8), type = "l", ylim = c(0.5, 10), log = "y", lwd = 1.5, ylab = "Relative sample size", xlab = expression(italic(p)[o]), las = 1) lines(z2p(zo), sampleSizeSignificance(zo = zo, designPrior = "predictive", power = 0.8), lwd = 2, lty = 2) lines(z2p(zo), sampleSizeSignificance(zo = zo, designPrior = "EB", power = 0.8), lwd = 1.5, lty = 3) legend("topleft", legend = c("conditional", "predictive", "EB"), title = "Design prior", lty = c(1, 2, 3), lwd = 1.5, bty = "n")