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 …

Background Missing data are unavoidable in epidemiological research, potentially leading
to bias and loss of precision. Multiple imputation (MI) is widely advocated as an …

Missing data are ubiquitous in medical research. Although there is increasing guidance on
how to handle missing data, practice is changing slowly and misapprehensions abound …

The goal of much observational research is to identify risk factors that have a causal effect
on health and social outcomes. However, observational data are subject to biases from …

Researchers faced with incomplete data are encouraged to consider whether their data are
'missing completely at random'(MCAR),'missing at random'(MAR) or 'missing not at …

Missing data are a pervasive problem in data analysis. Three common methods for
addressing the problem are (a) complete-case analysis, where only units that are complete …

Multiple imputation is a well-established general technique for analyzing data with missing
values. A convenient way to implement multiple imputation is sequential regression multiple …

Introduction Observational studies in health-related research often aim to answer causal
questions. Missing data are common in these studies and often occur in multiple variables …

With incomplete data, the “missing at random”(MAR) assumption is widely understood to
enable unbiased estimation with appropriate methods. While the need to assess the …

Causal inference from observational data is notoriously difficult, and relies upon many
unverifiable assumptions, including no confounding or selection bias. Here, we demonstrate …