2.5. Subgroup Meta-analysis
We intended to generate random-effects pooled effect estimates,
separately for RCTs and NRSIs, but only 3 RCTs and 2 NRSIs with
numerical data on primary outcomes were identified. Therefore: (i) we
generated frequentist (Mantel-Haenszel relative risk, Paul-Mandel for
τ2, Hartung-Knapp adjustment) and Bayesian [vaguely
informative prior for ln(RR) (mean=0, SD=4, half-normal for τ]
random-effects pooled estimates and prediction intervals specifically to
illustrate uncertainty (CI width) and heterogeneity of the RCT outcomes
(width of prediction intervals); (ii) adjusted proportions retrieved
from two NRSIs were used to calculate individual study risk ratios
(PVI/Cox-Maze) by the Miettinen-Nurminen method for more intuitive
presentation 17. We used package meta in R18 for the frequentist and package bayesmetafor Bayesian meta-analysis in R (R Core Team (2020). R: A language and
environment for statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. URL https://www.R-project.org/)19,20.