A new tool to help statisticians decide how to handle missing data in studies has been published by Bristol Biomedical Research Centre (BRC) researchers. In health research, it’s common to have missin...

Autistic people often have more health problems and a shorter life expectancy than non-autistic people. Healthcare differences may be a reason for this. For example, medicines that alter mood and brai...

Improving knowledge of adiposity’s genetic architecture across the lifecourse refines insights into its role in breast cancer.

Paul Allison explores the nuances of data missing at random (MAR).

Summary. Two common approaches to regression with missing covariates are complete-case analysis and ignorable likelihood methods. We review these approache

Our research provides strong evidence against the concerning claims made recently by US president Donald Trump.

The value of predictable improvement

Objective: Findings from family-based analyses, such as sibling comparisons, are often reported using only odds ratios or hazard ratios. We demonstrate how this can be improved upon by applying the ma...

Viktor H. Ahlqvist, postdoc at the Institute of Environmental Medicine (IMM), receives 3,000,000 SEK in a Postdoctoral Grant from the Swedish Society for Medical Research (SSMF) for the project “Advan...

Abstract. Auxiliary variables are used in multiple imputation (MI) to reduce bias and increase efficiency. These variables may often themselves be incomple

Researchers investigate how anti-seizure medication use during pregnancy may increase the risk of neuropsychological conditions in children.

Some antiseizure medications including valporate are associated with neurodevelopmental conditions in children exposed in utero but evidence is less clear for other drugs. Here the authors investigate...

Multiple imputation is a principled approach to account for missing data in analyses where valid results depends on careful construction of the imputation model. The potential for misspecification of ...