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Handling missing data in modelling quality of clinician-prescribed routine care: Sensitivity analysis of departure from missing at random assumption
(University of Nairobi, 2020)
Missing information is a major drawback in analyzing data collected in many routine health care settings. Multiple imputation assuming a missing at random mechanism is a popular method to handle missing data. The missing ...
Handling missing data in a composite outcome with partially observed components: simulation study based on clustered paediatric routine data
(University of Nairobi, 2021)
Composite scores are useful in providing insights and trends about complex and multidimensional quality of care processes. However, missing data in subcomponents may hinder the overall reliability of a composite measure. ...