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8 None of these approaches is statistically valid in general, and they can lead to serious bias. These include replacing missing values with values imputed from the observed data (for example, the mean of the observed values), using a missing category indicator, 7 and replacing missing values with the last measured value (last value carried forward). Statistical methods to handle missing dataĪ variety of ad hoc approaches are commonly used to deal with missing data. Therefore, biases caused by data that are missing not at random can be addressed only by sensitivity analyses examining the effect of different assumptions about the missing data mechanism. Unfortunately, it is not possible to distinguish between missing at random and missing not at random using observed data. Such biases can be overcome using methods such as multiple imputation that allow individuals with incomplete data to be included in analyses. When it is plausible that data are missing at random, but not completely at random, analyses based on complete cases may be biased. 6 This nomenclature is widely used, even though the phrases convey little about their technical meaning and practical implications, which can be subtle. Reasons for missing data are commonly classified as: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) (box 1). The risk of bias due to missing data depends on the reasons why data are missing. Furthermore, the cumulative effect of missing data in several variables often leads to exclusion of a substantial proportion of the original sample, which in turn causes a substantial loss of precision and power. However, results of such analyses can be biased. Researchers usually address missing data by including in the analysis only complete cases -those individuals who have no missing data in any of the variables required for that analysis. Finally, we describe the recent use and reporting of analyses using multiple imputation in general medical journals, and suggest guidelines for the conduct and reporting of such analyses. We discuss the circumstances in which multiple imputation may help by reducing bias or increasing precision, as well as describing potential pitfalls in its application. In this article, we review the reasons why missing data may lead to bias and loss of information in epidemiological and clinical research. Results based on this computationally intensive method are increasingly reported, but it needs to be applied carefully to avoid misleading conclusions. However, multiple imputation-a relatively flexible, general purpose approach to dealing with missing data-is now available in standard statistical software, 2 3 4 5 making it possible to handle missing data semiroutinely. 1 This is partly because statistical methods that can tackle problems arising from missing data have, until recently, not been readily accessible to medical researchers. Missing data are unavoidable in epidemiological and clinical research but their potential to undermine the validity of research results has often been overlooked in the medical literature. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them 6Department of Public Health and Primary Care, Institute of Public Health, Cambridge.5Medical Statistics Unit, London School of Hygiene and Tropical Medicine London, WC1E 7HT.4Cancer and Statistical Methodology Groups, MRC Clinical Trials Unit, London NW1 2DA.3Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, and University of Melbourne, Parkville, Victoria 3052, Australia.2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 0SR.1Department of Social Medicine, University of Bristol, Bristol BS8 2PR.James R Carpenter, reader in medical and social statistics 5.Angela M Wood, lecturer in biostatistics 6,.Michael G Kenward, professor of biostatistics 5,.John B Carlin, director of clinical epidemiology and biostatistics unit 3,.Jonathan A C Sterne, professor of medical statistics and epidemiology 1,.