Sensitivity Analysis of Informatively Coarsened Data Using Pattern Mixture Models

Abstract

We use the framework of coarsened data to motivate performing sensitivity analysis in the presence of incomplete data. To perform the sensitivity analysis, we specify pattern-mixture models to allow departures from the assumption of coarsening at random, a generalization of missing at random and independent censoring. We apply the concept of coarsening to address potential bias from missing data and interval-censored data in a randomized controlled trial of an herbal treatment for acute hepatitis. Computer code using SAS PROC NLMIXED for fitting the models is provided.

Key Words:

• Coarsened data
• Interval censoring
• Missing data
• Nonignorable missingness
• Sensitivity analysis

ACKNOWLEDGMENT

This research was supported by National Institutes of Health grant K12 HD043489.

Notes

Note. 95% CI Cover, empirical percent coverage of the 95% confidence interval.

Note. Bold indicates correct assumed values for φ₂. F(2) = 0.333, F(4) = 0.667. Standard error (SE), empirical standard deviation (ESD).