Abstract
Missing outcome data are encountered in many clinical trials and public health studies and present challenges in imputation. We present a simple and easy to use SAS-based imputation method for missing discrete outcome data. The method is based on minimum distance between baseline covariates of those with missing data and those without missing data. The imputation algorithm, a method that may be viewed as a variant of the hot dec imputation method, imputes missing values that are "close to" the observed values, implying that had there been data on those missing, it would have been similar to those non-missing. An illustrative example is presented.
| Original language | American English |
|---|---|
| Title of host publication | Proceedings of the Southeastern SAS Users Group |
| State | Published - Sep 1 2015 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Disciplines
- Biostatistics
- Community Health
- Public Health
Keywords
- Count data
- Hot dec
- Imputation
- Imputation of MRI data
- Mahalanobis Distance
- Missing discrete data
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