John Carlin develops biostatistics for health and medical research, including methods for missing data and causal inference. His institutional profiles describe work on multiple imputation and the need to specify the scientific purpose of an analysis before choosing a regression model.[ref]
https://www.vicbiostat.org.au/people/john-carlin. Research interests, imputation and scientific practice
[ref]https://www.mcri.edu.au/researcher-details/john-carlin. Methodological research and applications
With multiple imputation, several plausible completed datasets represent uncertainty about unobserved values. Their analyses must account both for uncertainty within a completed dataset and for variation between completed datasets. This makes the assumptions about the missing values visible in the analysis. Carlin is a coauthor of Bayesian Data Analysis by Andrew Gelman and others.[ref]
Bayesian Data Analysis by Andrew Gelman and others. Third-edition author list and missing-data methods
John Carlin
. Portrait identified on the linked official researcher profile; photographer and licence are not asserted. Source. New to topics? Read the docs here!