Transcription of Dealing with missing data: Key assumptions and methods …
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Dealing with missing data: Key assumptions and methods …
Technical Report No. 4 May 6, 2013 Dealing with missing data : Key assumptions andmethods for applied analysisMarina paper was published in fulfillment of the requirements for PM931 Directed Study in Health Policy and Managementunder Professor Cindy Christiansen s direction. Michal Horn y, Jake Morgan, Kyung Min Lee, and Meng-YunLin provided helpful reviews and 1 Contents Executive Summary .. 2 Acronyms .. 3 1. Introduction .. 4 2. missing data mechanisms .. 5 3. Patterns of 6 4. methods for handling missing data .. 6 Conventional methods .. 6 Listwise deletion (or complete case analysis ): .. 6 Imputation methods : .. 6 Advanced methods .. 7 Multiple Imputation .. 7 Maximum Likelihood .. 8 Other advanced methods .. 9 Bayesian simulation methods .. 9 Hot deck imputation 10 5. Dealing with missing data using SAS .. 10 Multiple Imputation (MI) .. 11 Maximum Likelihood (ML) .. 13 6. Dealing with missing data using STATA .. 15 Multiple imputation.
4.1.1. Listwise deletion (or complete case analysis): If a case has missing data for any of the variables, then simply exclude that case from the analysis. It is usually the default in statistical packages. (Briggs et al.,2003). Advantages: It can be used with any kind of statistical analysis and no special computational methods are required.
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