From 173722c9f01b8971eefa251f1ccee740224aedf3 Mon Sep 17 00:00:00 2001 From: wolbersm <90774360+wolbersm@users.noreply.github.com> Date: Fri, 19 Jan 2024 18:17:01 +0100 Subject: [PATCH] Update vignettes/stat_specs.Rmd Co-authored-by: Craig Gower-Page 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 8b3acdc3..684fb9c7 100644 --- a/vignettes/stat_specs.Rmd +++ b/vignettes/stat_specs.Rmd @@ -443,7 +443,7 @@ All approaches provide frequentist consistent estimates of the standard error fo For reference-based imputation methods, the situation is more complicated and two different types of variance estimators have been proposed in the statistical literature (@Bartlett2021). The first is the frequentist variance which describes the actual repeated sampling variability of the estimator. If the reference-based missing data assumption is correctly specified, then the resulting inference based on this variance is correct in the frequentist sense, i.e. hypothesis tests have asymptotically correct type I error control and confidence intervals have correct coverage probabilities under repeated sampling (@Bartlett2021, @Wolbers2021). -Reference-based missing data assumption are strong and borrow information from the reference arm for imputation in the active arm. As a consequence, the size of frequentist standard errors for treatment effects may decrease with increasing amounts of missing data. +Reference-based missing data assumptions are strong and borrow information from the reference arm for imputation in the active arm. As a consequence, the size of frequentist standard errors for treatment effects may decrease with increasing amounts of missing data. The second proposal is the so-called "information-anchored" variance which was originally proposed in the context of sensitivity analyses (@CroEtAl2019). This variance estimator is based on disentangling point estimation and variance estimation altogether. The information-anchoring principle described in @CroEtAl2019 states that the relative increase in the variance of the treatment effect estimator under MAR imputation with increasing amounts of missing data should be preserved for reference-based imputation methods. The resulting information-anchored variance is typically very similar to the variance under MAR imputation and typically increases with increasing amounts of missing data.