SP0156 MISSING DATA: IS IT ALL THE SAME?
Authors: Stian Lydersen
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Introduction:</h3> In most studies, there are "holes" in the data set, such that data are missing partly or completely for some of the subjects. This results in reduced statistical power. More seriously, missing data may cause biased results, since data are usually not missing completely at random. <h3>Missing data mechanisms:</h3> In which way, and to what extent, does the probability that data are missing, depend on observed and/or unobserved data values? This is called the missing data mechanism, and is important for an appropriate choice of analysis method. Three types of missing data mechanisms have been defined, see for example (Sterne et al. 2009). <h3>MCAR:</h3> <i>Missing completely at random</i>. There are no systematic differences between the missing values and the observed values. The probability that values are missing does not depend on any of the data, observed or unobserved. <h3>MAR:</h3> <i>Missing at random</i>. The probability that values are missing may depend on observed data values, but not on unobserved data values. For example, some variables are missing more frequentely for patients with higher age. Data are missing at random <i>conditionally</i> on observed data. <h3>MNAR:</h3> <i>Missing not at random.</i> Even after the observed data are taken into account, systematic differences remain between the missing values and the observed values: The probability that values are missing depends on unobserved data values as well. It is possible to distinguish between MCAR and MAR by inspecting the data at hand. But it is never possible to determine whether data are MNAR from your data. Methods for handling missing data Some commonly used methods, from the less to the more complex ones, are listed below, with an indication of when they give unbiased estimates: Complete case analysis (disregarding cases with partially missing data) (MCAR) Single imputation methods (sometimes under MAR but underestimates uncertainty) Multiple imputation (MAR) Full information maximum likelihood (MAR) Linear mixed models in longitudinal studies (MAR) In longitudinal studies, Last observation carried forward (LOCF) is a simple method for imputing missing values. But it is not unbiased under any sensible assumptions, and should not be used (Lydersen 2019). <h3>Reporting:</h3> In general, report the amount of missing data in the different variables, and how this was handled in the analysis. This is recommended in the Consort Guidelines (Moher et al. 2010) as well as the STROBE Statement (STROBE 2014), see also (Lydersen 2015) <h3>References:</h3> [1] Lydersen, S. 2015. Statistical review: frequently given comments. Ann.Rheum.Dis., 74, (2) 323-325. Lydersen, S. 2019.Last observation carried forward - simple but not recommended. In press,Tidsskrift for Den norskelegeforening [2] Moher, D., Hopewell, S., Schulz, K.F., Montori, V., Gotzsche, P.C., Devereaux, P.J., Elbourne, D., Egger, M., &amp; Altman, D.G. 2010. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ, 340, c869 [3] Sterne, J.A., White, I.R., Carlin, J.B., Spratt, M., Royston, P., Kenward, M.G., Wood, A.M., &amp; Carpenter, J.R. 2009. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ, 338, b2393 [4] STROBE Statement 2014. Strengthening the reporting of observational studies in epidemiology. <h3>Disclosure of Interests:</h3> None declared