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Updated: Dec 31, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Risk Adjustment, Mode Adjustment, and Nonresponse Bias Analysis on Quality Measures From a Long-Term Care Hospital
Sara M Zuckerbraun1, Anne Deutsch2, Celia Eicheldinger3
1Social, Statistical and Environmental Sciences, Survey Research Division, RTI International, Research Triangle Park, North Carolina.
Objectives:
To develop a patient risk adjustment model for experience of care (EOC) quality measures for long-term care hospitals (LTCHs) that includes mode of survey administration. To assess presence of nonresponse bias in the adjusted facility-level scores.
Design:
We tested 3 modes of collecting the EOC data: mail-only, mixed (ie, mail with telephone follow-up), and in-facility. This study used sequential modeling and impact analysis, specified a risk and mode adjustment model, and evaluated presence of nonresponse after adjustment.
Setting:
LTCHs.
Participants:
Patients (N=1364) and 69 LTCHs.
Intervention:
Not applicable.
Main Outcome Measures:
Risk and mode adjusted responses to 28 survey questions and 6 facility-level scores derived from survey responses.
Results:
Mode of data collection and patient risk variables (age, sex, overall health, overall mental health, marital status, education, race, and whether a proxy responded) were included in the model. Clinical variables were not significant. The in-facility mode was associated with significantly higher performance scores than the other modes. When the recommended risk and mode adjustment model was applied, nonresponse bias was not observed in any mode.
Conclusions:
LTCH EOC data should be adjusted for patient risk variables including mode of data collection.
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