Estimate Unit Information Standard Deviation

Estimates the unit information standard deviation (UISD) from sample sizes and standard errors. The UISD is used to scale weakly informative prior distributions for meta-analytic parameters.

Usage

estimate_unit_information_sd(sei, ni)

Arguments

sei

a complete numeric vector of strictly positive standard errors for each study.

ni

a complete numeric vector of strictly positive sample sizes for each study, with the same length as sei.

Value

Returns a single numeric value representing the estimated unit information standard deviation.

Details

The unit information standard deviation is computed following Equation 6 in Röver et al. (2021) : $$\text{UISD} = \sqrt{\frac{\sum n_i}{\sum \text{se}_i^{-2}}}$$ where \(n_i\) is the sample size and \(\text{se}_i\) is the standard error for each study.

This function is useful when you want to compute the UISD once and pass it to multiple analyses via the prior_unit_information_sd argument in brma() or related functions. This ensures consistent prior scaling across analyses performed on different subsets of the same data.

References

Röver C, Bender R, Dias S, Schmid CH, Schmidli H, Sturtz S, Weber S, Friede T (2021). “On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis.” Research Synthesis Methods, 12(4), 448–474. doi:10.1002/jrsm.1475 .

Examples

# Example with simulated data
sei <- c(0.2, 0.3, 0.25, 0.15)
ni  <- c(50, 30, 40, 80)
estimate_unit_information_sd(sei = sei, ni = ni)
#> [1] 1.439217