Litcius/Paper detail

rbmi: A R package for standard and reference-basedmultiple imputation methods

Craig Gower‐Page, Alessandro Noci, Marcel Wolbers

2022The Journal of Open Source Software17 citationsDOIOpen Access PDF

Abstract

Many randomized controlled clinical trials compare a continuous outcome variable that is assessed longitudinally at scheduled follow-up visits between subjects assigned to a intervention treatment group and those assigned to a control group. Missing outcome measurements may occur because subjects miss an assessment or drop out from the trial altogether. Moreover, intercurrent events (ICEs) such as discontinuations of the assigned treatment or initiations of rescue medications may affect the interpretation or the existence of the outcome measurements associated with the clinical question of interest. The ICH E9(R1) addendum on estimands, a regulatory document published by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, presents a structured framework to link trial objectives to a precise description of the targeted treatment effect in the presence of ICEs and missing data (ICH E9 working group, 2019).

Topics & Concepts

Imputation (statistics)Computer scienceR packageInformation retrievalStatisticsMathematicsMissing dataProgramming languageMachine learningStatistical Methods and Bayesian InferenceStatistical Methods and InferenceStatistical Methods in Clinical Trials