A Novel Tool for Evaluating Effect Modification in Older Adults with ADRD Using Medicare Claims

2026-08-06

AI Summary

This study evaluates the impact of hospital-acquired complications on post-discharge home days among older adults with Alzheimer’s disease and related dementias (ADRD), addressing challenges posed by death-induced truncation of recovery trajectories and effect heterogeneity. To this end, the authors propose a robust analytical strategy—termed PD-Robust—that integrates principal stratification with pseudo-data techniques to yield interpretable causal estimates under a structural working model, while enabling model diagnostics, robustness checks, and characterization of covariate effects within principal strata. This approach represents the first synthesis of principal stratification and pseudo-data methods, filling a critical gap in real-world data analysis concerning death truncation and effect modification. Applied to Medicare data, it reveals that male ADRD patients under 85 experience the largest reduction in home days—up to 23 days—due to complications. Simulation studies confirm the method’s low bias and valid inference, and an accompanying R package is released to facilitate broader adoption.

Abstract

Studying consequences following baseline exposures has become increasingly important for advancing comparative effectiveness research using real-world data. This case study evaluates the impact of hospital-acquired conditions (HAC) during hospitalization for hip fracture on post-discharge recovery trajectories among older adults living with Alzheimer Disease and Related Dementia, a population particularly vulnerable to high post-hospital mortality. To appropriately account for truncation of recovery trajectory due to death and to explore heterogeneity in effect modification by patient demographics, we introduce a novel pseudo data-based robust (PD-Robust) analysis strategy, accompanied by an R package and detailed usage guidance to inform real data analysis. Grounded in an interpretable estimand via principal stratification under principal ignorability and a structural working model, PD-Robust accommodates truncation by death, provides model diagnosis and robustness check against assumption violation, and facilitates the characterization of patient profiles among the principal stratum. Applied to Medicare claims data, where better recovery is defined as more days at home (DAH) over six months post-discharge, PD-Robust reveals heterogeneity in HAC effects, with males under the age of 85 years as a high-risk subgroup experiencing up to 23 fewer DAH, comparing HAC to no HAC. This exceeds the 8-day threshold regarded as clinically meaningful difference in DAH due to any exposure. Moreover, simulation studies further demonstrate that PD-Robust achieves low estimation bias and accurate statistical inference, supporting its utility in real-world data applications.

Problem

• effect modification
• hospital-acquired conditions
• Alzheimer Disease and Related Dementia
• truncation by death
• recovery trajectory

Innovation

• PD-Robust
• principal stratification
• truncation by death
• effect modification
• real-world data