Evaluating the Role of High‐Dimensional Proxy Data in Confounding Adjustment in Multiple Sclerosis Research: A Case Study
Bibliographic record
Abstract
PURPOSE: Given the historical use of limited confounders in multiple sclerosis (MS) studies utilizing administrative health data, this brief report evaluates the impact of incorporating high-dimensional proxy information on confounder adjustment in MS research. We have implemented high-dimensional propensity score (hdPS) and high-dimensional disease risk score (hdDRS) methods to assess changes in effect estimates for the association between disease-modifying drugs (DMDs) and all-cause mortality in an MS cohort from British Columbia (BC), Canada. METHODS: We conducted a population-based retrospective study using linked administrative databases from BC, including health insurance registries, demographics, physician visits, hospitalizations, prescriptions, and vital statistics. The cohort comprised 19 360 individuals with MS, followed from January 1, 1996, to December 31, 2017. DMD exposure was defined as at least 180 days of use for beta-interferon or glatiramer acetate, or at least 90 days for other DMDs. The outcome was time to all-cause mortality. We compared Cox proportional hazards models adjusting for investigator-specified covariates with those incorporating additional empirical covariates using hdPS and hdDRS methods. RESULTS: In the unadjusted analysis, DMD exposure was associated with a 69% lower risk of mortality (HR 0.31; 95% CI: 0.27-0.36). Adjusting for investigator-specified covariates, the adjusted hazard ratio (aHR) was 0.76 (95% CI: 0.65-0.89). HdPS analyses showed a 20%-23% lower mortality risk (aHRs: 0.77 to 0.80), while hdDRS analyses indicated a 19%-21% reduction (aHRs: 0.79 to 0.81). CONCLUSIONS: Incorporating high-dimensional proxy information resulted in minor variations in effect estimates compared to traditional covariate adjustment. These findings suggest that the impact of residual confounding in the question under consideration may be modest. Further research should explore additional data dimensions and replicate these findings across different datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.218 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".