Reporting and variability of constructing medication treatment episodes in pharmacoepidemiology studies: A methodologic systematic review using the case study of <scp>DPP</scp>‐4 inhibitors and cardiovascular outcomes
Bibliographic record
Abstract
PURPOSE: In pharmacoepidemiologic studies, estimating medication adherence, persistence, and exposure patterns is critical. Constructing medication treatment episodes from prescription claims data involves assumptions related to grace period, carry-over, and lag effect, but there are no guidelines for these assumptions. We evaluated reporting and variability of these parameters in pharmacoepidemiology studies, using a case study of antihyperglycemic medications and major adverse cardiovascular events (MACE). METHODS: We conducted a systemic review using MEDLINE and EMBASE for studies published prior to January 2, 2020 comparing the risk of MACE between dipeptidyl peptidase 4 (DPP-4) inhibitors and active comparators. We extracted study characteristics and results, including grace period, carry-over, and lag effect. Risk of bias was assessed by the Newcastle-Ottawa scale, and assessments for prevalent user, immortal time, time lag, and time window biases. RESULTS: A total of 14/1850 studies identified were included. Grace period was not reported in 5 (35.7%) studies and ranged from 0 days to 180 days when reported. Carry-over was not reported in 10 studies (71.4%). Lag effect was not reported in nine (71.4%) studies and ranged from 0 days to 180 days when reported. No studies conducted sensitivity analyses examining the effects of these assumptions on study findings. Predominant biases were inadequate follow-up time, comparability of cohorts, prevalent use, and lag time bias. CONCLUSIONS: Use of grace period, carry-over, and lag effect were poorly reported and highly variable. Future pharmacoepidemiology studies should improve reporting, justify ranges for these parameters, and conduct sensitivity analyses to evaluate effects of these assumptions.
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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 | 0.043 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads 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".