Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Missing data are ubiquitous in medical research, yet there is still uncertainty over when
restricting to the complete records is likely to be acceptable, when more complex methods …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to
analyzing partially observed data In this comprehensively revised Second Edition of Multiple …

Background: The exploitation of synthetic data in health care is at an early stage. Synthetic
data could unlock the potential within health care datasets that are too sensitive for release …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

In many contexts, confidentiality constraints severely restrict access to unique and valuable
microdata. Synthetic data which mimic the original observed data and preserve the …

Multiple imputation is a straightforward method for handling missing data in a principled
fashion. This paper presents an overview of multiple imputation, including important …

Multiple imputation is particularly well suited to deal with missing data in large epidemiologic
studies, because typically these studies support a wide range of analyses by many data …

Predictive modeling is increasingly being employed to assist human decision-makers. One
purported advantage of replacing or augmenting human judgment with computer models in …