Impact of time-varying exposure on estimated effects in observational studies using routinely collected data: protocol for a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Time-varying exposure is an important issue that should be addressed in longitudinal observational studies using routinely collected data (RCD) for drug treatment effects. How well investigators designed, analysed and reported time-varying exposure, and to what extent the divergence that can be observed between different methods used for handling time-varying exposure in these studies remains uncertain. We will conduct a cross-sectional study to comprehensively address this question. METHODS AND ANALYSIS: We have developed a comprehensive search strategy to identify all studies exploring drug treatment effects including both effectiveness and safety that used RCD and were published in core journals between 2018 and 2020. We will collect information regarding general study characteristics, data source profile, methods for handling time-varying exposure, results and the interpretation of findings from each eligibility. Paired reviewers will screen and extract data, resolving disagreements through discussion. We will describe the characteristics of included studies, and summarise the method used for handling time-varying exposure in primary analysis and sensitivity analysis. We will also compare the divergence between different approaches for handling time-varying exposure using ratio of risk ratios. ETHICS AND DISSEMINATION: No ethical approval is required because the data we will use do not include individual patient data. Findings will be disseminated through peer-reviewed publications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".