Missing data analysis using multiple imputation: getting to the heart of the matter.
Missing data are a pervasive problem in health investigations. We describe some background of missing data analysis and criticize ad hoc methods that are prone to serious problems. We then focus on multiple imputation, in which missing cases are first filled in by several sets of plausible values to create multiple completed datasets, then standard complete-data procedures are applied to each completed dataset, and finally the multiple sets of results are combined to yield a single inference. We introduce the basic concepts and general methodology and provide some guidance for application. For illustration, we use a study assessing the effect of cardiovascular diseases on hospice discussion for late stage lung cancer patients.
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Yulei
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Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115, USA. he@hcp.med.harvard.edu
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006302:Health Services Research / Q000706:statistics & numerical data*; D017051:Hospice Care / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008175:Lung Neoplasms / Q000628:therapy; D008297:Male; D008875:Middle Aged; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data*; D012984:Software; D016896:Treatment Outcome; D055815:Young Adult