Uncertainty intervals for mixed effects models with non-ignorable missingness
(English)Manuscript (preprint) (Other academic)
When estimating regression models with missing outcomes, scientists usually rely on strong untestable assumptions such as missing at random, exclusion restrictions, or distributional assumptions on the missing data, in order to point identify the parameters of interest. An alternative is to estimate identification intervals under milder assumptions. In this paper, we use a sensitivity parameter, which quantifies the non-ignorability of the missingness mechanism, in order to estimate identification intervals for regression parameters in linear mixed effects models. By taking sampling variability into account, we obtain uncertainty intervals which can be used for a sensitivity analysis of the missing at random assumption or to draw conclusions from the data without making unnecessarily strong assumptions.
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-127120OAI: oai:DiVA.org:umu-127120DiVA: diva2:1043966