An empirical assessment of immeasurable time bias in the setting of nested case‐control studies: Statins and all‐cause mortality among patients with heart failure
Bibliographic record
Abstract
PURPOSE: Immeasurable time bias exaggerates drug benefits in pharmacoepidemiologic studies due to exposure misclassification that occurs due to the lack of inpatient drug data in many healthcare databases. METHODS: To estimate the magnitude of immeasurable time bias and assess potential approaches to minimize it, we conducted a nested case-control study of statin use and mortality among heart failure patients using the South Korean nationwide healthcare database, which contains both inpatient and outpatient medication data. Using both inpatient and outpatient medication data to define the gold standard exposure definition, we assessed 10 different analytical methods in which exposure was defined using outpatient medication data only. We compared different methodological approaches to reduce immeasurable time bias: restricting to nonhospitalized patients, adjusting for hospitalization, weighting by either measurable time (nonhospitalized time during 90-d period) or outpatient time, and computing the odds ratios (ORs) using 90-day cumulative probability of exposure produced by the Kaplan-Meier product-limit estimator for cases and controls. RESULTS: The three approaches that most closely approximated the gold standard (hazard ratio [HR] 1.20; 95% confidence interval [CI], 1.05-1.37) were weighting by either measurable (HR 1.09; 95% CI, 0.92-1.28) or outpatient time (HR 1.14; 95% CI, 0.96-1.34) in the unexposed or by estimating the 90-day exposure probability (HR 1.31; 95% CI, 1.11-1.51). CONCLUSION: The use of one of these three methods may be suggested as an approach to minimize immeasurable time bias in nested case-control studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".