Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach

Chinchin Wang1 Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, Canada;2 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, H3A 1A2, CanadaView further author information

Tyrel Stokes3 Department of Mathematics and Statistics, McGill University, Montreal, Quebec, H3A 0B9, CanadaView further author information

Russell J Steele3 Department of Mathematics and Statistics, McGill University, Montreal, Quebec, H3A 0B9, CanadaView further author information

Niels Wedderkopp4 Orthopedic Department University Hospital of South West Denmark, Department of Regional Health Research, University of Southern Denmark, Odense, Denmark

https://orcid.org/0000-0002-9660-6618 View further author information

Ian Shrier1 Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, CanadaCorrespondence[email protected]
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Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach

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Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach

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