Background Missing data are common in medical research, which can lead to a loss in
statistical power and potentially biased results if not handled appropriately. Multiple …

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future
outcome for individual patients are abundant in the medical literature; however, systematic …

Objectives Researchers are concerned whether multiple imputation (MI) or complete case
analysis should be used when a large proportion of data are missing. We aimed to provide …

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

Background The possible therapeutic impact of dietary changes on existing mental illness is
largely unknown. Using a randomised controlled trial design, we aimed to investigate the …

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 …

Missing data are a frequent problem in cost-effectiveness analysis (CEA) within a
randomised controlled trial. Inappropriate methods to handle missing data can lead to …

Multiple imputation by chained equations is a flexible and practical approach to handling
missing data. We describe the principles of the method and show how to impute categorical …

Purpose: Lesbian, gay, bisexual, and transgender (LGBT) people are a health disparate
population as identified in Healthy People 2020. Yet, there has been limited attention to how …

Multiple imputation for missing data in epidemiological and clinical research: potential and
pitfalls | The BMJ Skip to main content Intended for healthcare professionals Access provided by …