Sensitivity analysis for multivariable missing data using mu…

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  • DOI 10.48550/arxiv.2502.02892
  • OpenAlex ID W4407212429
  • Type Preprint
  • Language EN
  • Published 05 Feb 2025
  • Source arXiv (Cornell University)
  • Publisher Cornell University
  • Countries 3
  • Institutions 6
  • Added 07 Feb 2025
  • Updated 16 Oct 2026
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Sensitivity analysis for multivariable missing data using multiple imputation: a tutorial

Preprint 2025 EN Green

Multiple imputation is a popular method for handling missing data, with fully conditional specification (FCS) being one of the predominant imputation approaches for multivariable missingness. Unbiased estimation with standard implementations of multiple imputation depends on assumptions concerning the missingness mechanism (e.g. that data are "missing at random"). The plausibility of these assumpt…

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5 authors
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1 Cattram Duong Nguyen The University of Melbourne (AU) Murdoch Children's Research In… (AU)
2 Katherine J. Lee The University of Melbourne (AU) Murdoch Children's Research In… (AU)
3 Ian R. White MRC Clinical Trials Unit at UC… (GB) University College London (GB)
4 Stef van Buuren Netherlands Organisation for A… (NL) Utrecht University (NL)
5 Margarita Moreno‐Betancur The University of Melbourne (AU) Murdoch Children's Research In… (AU)
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