Assessment of risk of bias is regarded as an essential component of a systematic review on
the effects of an intervention. The most commonly used tool for randomised trials is the …

… Ian R White, senior scientist2, … Carpenter JR, Kenward MG, White IR. Sensitivity
analysis after multiple imputation under missing at random: a weighting approach. Stat …

Multiple imputation by chained equations is a flexible and practical approach to handling
missing data. We describe the principles of the method and show how to impute categorical …

Background The associations of blood pressure with the different manifestations of incident
cardiovascular disease in a contemporary population have not been compared. In this study, …

The simplest approach to dealing with missing data is to restrict the analysis to complete
cases, ie individuals with no missing values. This can induce bias, however. Inverse probability …

The present guideline summarizes all aspects of patch testing for the diagnosis of contact
allergy in patients suspected of suffering, or having been suffering, from allergic contact …

Missing data are a common occurrence in real datasets. For epidemiological and prognostic
factors studies in medicine, multiple imputation is becoming the standard route to estimating …

Simulation studies are computer experiments that involve creating data by pseudo‐random
sampling. A key strength of simulation studies is the ability to understand the behavior of …

Network meta‐analysis (multiple treatments meta‐analysis, mixed treatment comparisons)
attempts to make the best use of a set of studies comparing more than two treatments. …

Multiple imputation is commonly used to impute missing data, and is typically more efficient
than complete cases analysis in regression analysis when covariates have missing values. …