From 0fa455f91d631577bcdc94e543da19a3db3b13fa Mon Sep 17 00:00:00 2001 From: wolbersm <90774360+wolbersm@users.noreply.github.com> Date: Thu, 18 Jan 2024 12:25:45 +0100 Subject: [PATCH] Update vignettes/stat_specs.Rmd Co-authored-by: Alessandro Noci <59877596+nociale@users.noreply.github.com> Signed-off-by: wolbersm <90774360+wolbersm@users.noreply.github.com> --- vignettes/stat_specs.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/stat_specs.Rmd b/vignettes/stat_specs.Rmd index 356cbf32..2161ad15 100644 --- a/vignettes/stat_specs.Rmd +++ b/vignettes/stat_specs.Rmd @@ -461,7 +461,7 @@ Reference-based Bayesian (or approximate Bayesian) multiple imputation methods c A frequentist variance for these methods could in principle be obtained via bootstrap or jackknife re-sampling of the treatment effect estimates but this would be very computationally intensive and is not directly supported by `rbmi`. Our view is that for primary analyses, accurate type I error control (which can be obtained by using the frequntist variance) is more important than adherence to the information anchoring principle which, to us, is -not fully compatible with the strong reference-based missing data assumptions. In any case, if reference-based imputation is used for the primary analysis, it is critical that the chosen +not fully compatible with the strong reference-based assumptions. In any case, if reference-based imputation is used for the primary analysis, it is critical that the chosen reference-based assumption can be clinically justified, and that suitable sensitivity analyses are conducted to stress-test these assumptions. Conditional mean imputation combined with the jackknife is the only method which leads to deterministic standard error estimates and, consequently, confidence intervals and $p$-values are also deterministic. This is particularly important in a regulatory setting where it is important to ascertain whether a calculated $p$-value which is close to the critical boundary of 5% is truly below or above that threshold rather than being uncertain about this because of Monte Carlo error.