High‐Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome
Bibliographic record
Abstract
PURPOSE: Health administrative databases often contain no information on some important confounders, leading to residual confounding in the effect estimate. We aimed to explore the performance of high-dimensional disease risk score (hdDRS) to deal with residual confounding bias for estimating causal effects with survival outcomes. METHODS: We used health administrative data of 49 197 individuals in British Columbia to examine the relationship between tuberculosis infection and time-to-development of cardiovascular disease (CVD). We designed a plasmode simulation exploring the performance of eight hdDRS methods that varied by different approaches to fit the risk score model and also examined results from high-dimensional propensity score (hdPS) and traditional regression adjustment. The log-hazard ratio (log-HR) was the target parameter with a true value of log(3). RESULTS: In the presence of strong unmeasured confounding, the bias observed was -0.11 for the traditional method and -0.047 for the hdPS method. The bias ranged from -0.051 to -0.058 for hdDRS methods when risk score models were fitted to the full cohort and -0.045 to -0.049 when risk score models were fitted only to unexposed individuals. All methods showed comparable standard errors and nominal bias-eliminated coverage probabilities. With weak unmeasured confounding, hdDRS and hdPS produced approximately unbiased estimates. Our data analysis, after addressing residual confounding, revealed an 8%-11% higher CVD risk associated with tuberculosis infection. CONCLUSIONS: Our findings support the use of selected hdDRS methods to address residual confounding bias when estimating treatment effects with survival outcomes. In particular, the hdDRS method using rate-based risk score modeling on unexposed individuals consistently exhibited the least bias. However, the hdPS method showed comparable performance across most evaluated scenarios. We share reproducible R codes to facilitate researchers' adoption and further evaluation of these methods.
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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 | 0.063 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".