Traditional <i>versus</i> marginal structural models to estimate the effectiveness of <i>β</i>‐blocker use on mortality after myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Observational studies of the effect of beta-blockers on all-cause mortality after an acute myocardial infarction (AMI) have tended to overestimate the effectiveness of this treatment. OBJECTIVE: To compare the estimates of the effect of beta-blocker use on mortality post-AMI derived from a traditional adjusted regression model with those from a marginal structural model. METHODS: A population-based cohort spanning the period of 2002-2004 was formed from the United Kingdom General Practice Research Database (GPRD). The cohort included all subjects who survived 90 days after their first AMI, who were then followed for 9 months. beta-Blocker use and blood pressure were identified in both the 90-day period before and the 90-day period after the AMI. Rate ratios (RR) were estimated using pooled logistic regression. RESULTS: The cohort included 9939 participants who survived 90 days after their AMI, of whom 633 died during the 9-month follow-up. Over 23% were taking beta-blockers pre-AMI, compared with 71% post-AMI. Using the traditional adjusted regression analysis, the RR of death with post-AMI beta-blocker use was 0.54 (95% confidence interval (CI): 0.45-0.67), while using the inverse probability of treatment weighting (IPTW) model it was 0.72 (95%CI: 0.61-0.84). The IPTW estimate is compatible with the estimate derived from a meta-analysis of randomized controlled trials (RCTs) while the adjusted regression estimate exaggerates the effectiveness. CONCLUSIONS: Observational studies of the association of anti-hypertensive medications with all-cause mortality should consider adding a marginal structural model to their armamentarium of data analysis.
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 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 | 0.003 | 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 | 0.001 |
| 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; 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".