The Extent and Magnitude of Bias in Case-Crossover Studies of Real-World Non-transient Medications Patterns: A Simulation Study with Real-World Examples
Bibliographic record
Abstract
INTRODUCTION: A case-crossover study is a self-controlled design most appropriate for evaluating transient medication exposures. However, it has increasingly been used in studies of chronic medications and can cause bias in effect estimates that vary based on the pattern of medication use. The goal of this study was to evaluate the magnitude of this bias across different medication-use patterns. OBJECTIVE: To quantify the magnitude of the bias introduced by different medication patterns and evaluate different case-crossover approaches to mitigate the bias. METHODS: We conducted a simulation study evaluating the bias introduced by (1) seven common medication patterns separately, and (2) cohort with 15 different patterns combined. We evaluated each scenario under risk ratios of 0.50, 0.75, 1.00, 1.50, and 2.00. Each approach was analyzed using conditional logistic regression comparing the probability of exposure on the outcome day to 30 days prior. A case-time-control design was used in each of the scenarios. Sensitivity analysis was performed to evaluate the impact on the estimates when changing the length of the risk and control windows. We conducted a real-world example focusing on sodium-glucose co-transporter-2 inhibitor users as real-world examples. RESULTS: The case-crossover design resulted in unbiased estimates when patterns were consistent with transient exposures but were biased upward with prolonged exposure patterns. The magnitude of the bias varies by patterns or pattern combinations. When evaluating prolonged exposures individually or combined as a cohort with mixture patterns, case-time-control with extended risk and control window (30 days) produced unbiased results (mean bias ≤ 0.03). CONCLUSION: Researchers who use the case-crossover design to evaluate non-transient exposures should implement recommended methods to account for biases.
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 | 0.177 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".