Abstract
Logically defined outcomes are commonly used in medical diagnoses and epidemiological research. When missing values in the original outcomes exist, the method of handling the missingness can have unintended consequences, even if the original outcomes are missing completely at random. In this note, we consider 2 binary original outcomes, which are missing completely at random. For estimating the prevalence of a logically defined "or" outcome, we discuss the properties of 4 estimators: the complete-case estimator, the available-case estimator, the maximum likelihood estimator (MLE), and a moment-based estimator. With the exception of the available-case case estimator, all the estimators are consistent. The MLE exhibits superior performance and should be generally adopted.
Original language | English (US) |
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Pages (from-to) | 800-804 |
Number of pages | 5 |
Journal | Biostatistics |
Volume | 8 |
Issue number | 4 |
DOIs | |
State | Published - Oct 2007 |
Keywords
- Available-case estimator
- Complete-case estimator
- Hypertension
- Maximum likelihood estimator
- Missing data
- Moment-based estimator
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty