Update vignettes/stat_specs.Rmd

Co-authored-by: Craig Gower-Page <[email protected]>
Signed-off-by: wolbersm <[email protected]>

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.