Creates a design prior for the effect size which can then be used for power and sample size calculations of a replication study. The design prior is obtained from updating an initial prior for the effect size by the data from the original study. A normal-normal hierarchical model is assumed, see Pawel et al. (2022) for details.
designPrior(
to,
so,
mu = 0,
sp = Inf,
tau = 0,
g = sp^2/(tau^2 + so^2),
h = tau^2/so^2,
type = c(NA, "conditional", "predictive", "EB")
)Returns an object of class "designPrior" which is a list containing:
dpMean | The computed mean of the design prior |
dpVar | The computed variance of the design prior |
to | The specified original effect estimate |
so | The specified original standard error |
mu | The specified mean of the initial prior |
sp | The specified standard deviation of the initial prior |
tau | The specified heterogeneity variance |
Effect estimate from original study
Standard error of effect estimate from original study
The initial prior mean. Defaults to 0
The initial prior standard deviation. Defaults to Inf (an
improper uniform prior)
The initial prior heterogeneity standard deviation. Defaults to
0 (no heterogeneity)
The relative initial prior variance g =
sp^2/(tau^2 + so^2) (alternative parametrization of
prior standard deviation sp)
The relative initial prior heterogeneity variance h =
tau^2/so^2 (alternative parametrization of prior
heterogeneity standard deviation tau)
Shortcut for special parameter combinations. The available
options are NA, "conditional", "predictive", and
"EB" (see details). Defaults to NA
Samuel Pawel
The "conditional" design prior corresponds to a point mass at
the original effect estimate, i.e., assuming that the true effect size is
equal to the original effect estimate. The "predictive" design
prior is obtained from updating a uniform initial prior by the likelihood
of the original data. The "EB" design prior is obtained by
empirical Bayes estimation of the variance of the normal prior and
induces adaptive shrinkage that depends on the p-value of the original
effect estimate.
Pawel, S., Consonni, G., and Held, L. (2022). Bayesian approaches to designing replication studies. arXiv preprint. tools:::Rd_expr_doi("10.48550/arXiv.2211.02552")
pors, ssd