Impact of a Patient’s Baseline Risk on the Relative Benefit and Harm of a Preventive Treatment Strategy: Applying Trial Results in Clinical Decision Making
Bibliographic record
Abstract
Background For translating an overall trial result into an individual patient’s expected absolute treatment effect, differences in relative treatment effect between patients need to be taken into account. The aim of this study was to evaluate whether relative treatment effects of medication in 2 large contemporary trials are influenced by multivariable baseline risk of an individual patient. Methods and Results In 9361 patients from SPRINT (Systolic Blood Pressure Intervention Trial), risk of major adverse cardiovascular events was assessed using a newly derived risk model. In 18 133 patients from the RE‐LY (Randomized Evaluation of Long‐Term Anticoagulant Therapy) trial, risk of stroke or systemic embolism and major bleeding was assessed using the Global Anticoagulant Registry in the Field–Atrial Fibrillation risk model. Heterogeneity of trial treatment effect was assessed using Cox models of trial allocation, model linear predictor, and their interaction. There was no significant interaction between baseline risk and relative treatment effect from intensive blood pressure lowering in SPRINT ( P =0.92) or from dabigatran compared with warfarin for stroke or systemic embolism in the RE‐LY trial ( P =0.71). There was significant interaction between baseline risk and treatment effect from dabigatran versus warfarin in the RE‐LY trial ( P <0.001) for major bleeding. Quartile‐specific hazard ratios for bleeding ranged from 0.40 (95% CI, 0.26–0.61) to 1.04 (95% CI, 0.83–1.03) for dabigatran, 110 mg, and from 0.61 (95% CI, 0.42–0.88) to 1.20 (95% CI, 0.97–1.50) for dabigatran, 150 mg, compared with warfarin. Conclusions Effect modification of relative treatment effect by individual baseline event risk should be assessed systematically in randomized clinical trials using multivariate risk prediction, not only in terms of treatment efficacy but also for important treatment harms, as a prespecified analysis. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01206062.
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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 | 0.491 | 0.740 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".