Pharmacoepidemiology and drug safety · 2025
Simultaneously Dealing With Immortal Time Bias and Residual Confounding: A Case Study of a High-Dimensional Propensity Score Approach With a Nested Case-Control Framework in Multiple Sclerosis Research.
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Hossain Md Belal, Ng Huah Shin, Zhu Feng, Tremlett Helen, Karim Mohammad Ehsanul
Аннотация
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.
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.