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B1159
Title: Missing data and multiple imputation in clinical epidemiological research Authors:  Irene Petersen - UCL (United Kingdom) [presenting]
Abstract: Missing data are ubiquitous in clinical epidemiological research and often found in electronic health records. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. Several ad-hoc methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the missing completely at random (MCAR) assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data and provides unbiased and valid estimates of associations based on information from the available data. We will discuss the different methods for dealing with missing data in clinical epidemiology.