Validating the effects of drug treatment on blood pressure in the General Practice Research Database
Bibliographic record
Abstract
PURPOSE: Observational studies using clinical databases, such as the United Kingdom's General Practice Research Database (GPRD), may provide an alternative to clinical trial data for detecting longitudinal changes in blood pressure due to drug exposures that vary over time. Blood pressure data which are measured at variable intervals and are often missing present a particular methodological challenge to the analysis of such studies. METHODS: To assess effects on blood pressure, we extracted from the GPRD several cohorts of new drug users of warfarin (n = 21,532), ibuprofen (n = 92,037), proton pump inhibitors (n = 153,695), statins (n = 118,704), rofecoxib (n = 6399), and celecoxib (n = 6217) from 2001 to 2003. Several blood pressure readings were missing either before or after initiating therapy. We compared the results of analyses using a linear mixed model with a pre-post quasi-experimental design, using the multiple imputation approach to account for missing data. RESULTS: There was evidence that the missing blood pressure data were not missing completely at random as subjects with more blood pressure readings tended to have higher recorded values. For statins, the mixed model estimated a change in systolic blood pressure of -3.80 mmHg (99% confidence interval (CI): from -3.97 to -3.63), similar to the quasi- experimental model and to the -4.00 mmHg estimated from clinical trials. Sensitivity analyses indicate that these estimates are robust. For rofecoxib, the change in systolic blood pressure were 2.20 mmHg (99%CI: 1.09-3.32) and 1.21 mmHg (99%CI: 0.21-2.22) for the two methods, respectively, again confirming the findings of randomized trials. CONCLUSION: With appropriate statistical techniques, GPRD blood pressure data can be used to estimate blood pressure changes secondary to drug therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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; 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".