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Updated: Sep 18, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Simultaneously Dealing With Immortal Time Bias and Residual Confounding: A Case Study of a High-Dimensional
Md Belal Hossain1,2, Huah Shin Ng3,4, Feng Zhu5
1School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.
Background:
Observational studies of time-dependent treatments often face immortal time bias and residual confounding, complicating treatment effect estimation. We implemented a high-dimensional propensity score (hdPS) analysis within a nested case-control (NCC) framework to address both biases simultaneously.
Methods:
We used a retrospective cohort of 19 360 individuals with multiple sclerosis (MS) in British Columbia, Canada, to examine the relationship between disease-modifying drugs (DMDs) and all-cause mortality. A 1:4 NCC analysis addressed immortal time bias, and hdPS was applied to handle residual confounding. Sensitivity analyses tested the robustness of findings across various hdPS parameters and matching strategies.
Results:
We matched a total of 3209 cases to 12 293 controls in the NCC analysis, and demonstrated a 28% reduction in mortality risk associated with exposure to DMDs (hazard ratio [HR]: 0.72, 95% confidence interval [CI]: 0.62-0.84) in the NCC-hdPS analysis. Sensitivity analyses using different propensity score estimation techniques and control-matching strategies yielded consistent results, with HRs ranging between 0.70 and 0.77.
Conclusions:
This study offers a practical framework for addressing immortal time bias and residual confounding simultaneously, improving the validity of effect estimates in real-world studies. We shared reproducible R codes for researchers to facilitate the adoption of this methodology in their research.
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