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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.148 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".