Standard and reference-based conditional mean imputation.

Marcel Wolbers ORCID logo; Alessandro Noci; Paul Delmar; Craig Gower-Page; Sean Yiu; Jonathan W Bartlett ORCID logo; (2022) Standard and reference-based conditional mean imputation. Pharmaceutical statistics, 21 (6). pp. 1246-1257. ISSN 1539-1604 DOI: 10.1002/pst.2234
Copy

Clinical trials with longitudinal outcomes typically include missing data due to missed assessments or structural missingness of outcomes after intercurrent events handled with a hypothetical strategy. Approaches based on Bayesian random multiple imputation and Rubin's rules for pooling results across multiple imputed data sets are increasingly used in order to align the analysis of these trials with the targeted estimand. We propose and justify deterministic conditional mean imputation combined with the jackknife for inference as an alternative approach. The method is applicable to imputations under a missing-at-random assumption as well as for reference-based imputation approaches. In an application and a simulation study, we demonstrate that it provides consistent treatment effect estimates with the Bayesian approach and reliable frequentist inference with accurate standard error estimation and type I error control. A further advantage of the method is that it does not rely on random sampling and is therefore replicable and unaffected by Monte Carlo error.


picture_as_pdf
PharmaceuticalStatistics2022WolbersStandardandreference‐basedconditionalmeanimputation.pdf
subject
Published Version
Available under Creative Commons: 4.0

View Download

Atom BibTeX OpenURL ContextObject in Span Multiline CSV OpenURL ContextObject Dublin Core Dublin Core MPEG-21 DIDL EndNote HTML Citation JSON MARC (ASCII) MARC (ISO 2709) METS MODS RDF+N3 RDF+N-Triples RDF+XML RIOXX2 XML Reference Manager Refer Simple Metadata ASCII Citation EP3 XML
Export

Downloads