Abstract
Classifying patients based on stated reasons for missing outcome from different intercurrent events induces patients’ subsets in data from clinical trials. Often, data imputation disregards these patients’ subsets. We discuss a non-parametric data imputation method that reflects reasons stated for missing data and hence patients’ subsets. This subset imputation method is based on a similarity measure between baseline covariates of patients’ subset with missing data and a random closest subset without missing data. An illustration using imputation of gadolinium enhancing lesions in multiple sclerosis is provided.
| Original language | American English |
|---|---|
| Title of host publication | Proceedings of the Biopharmaceutical Section of the American Statistical Association |
| State | Published - Jul 30 2019 |
Disciplines
- Biostatistics
- Environmental Public Health
- Epidemiology
- Public Health
Keywords
- Intercurrent Event; Missing at Random; Missing Data; Minimum Subset Imputation; Discrete Data Imputation; Count Data Imputation
