Abstract 197: Identification and Evaluation of Five Meta-Analytic Statistical Procedures to Validate Surrogate End Points for Use in Randomized Controlled Trials
Bibliographic record
Abstract
Introduction: Although randomized trials for atherosclerosis widely measure treatment effects on biomarkers or surrogate end points in order to reduce follow-up duration and study costs, treatment effects on surrogate end points do not always predict treatment effects on patient outcomes. It is difficult to validate whether beneficial treatment effects on surrogate end points translate to beneficial effects on patient outcomes since gold-standard statistical procedures for surrogate end point validation require large amounts of individual patient data which is not easily available to investigators designing clinical trials. Objectives: We sought to identify and evaluate meta-analytic statistical procedures for surrogate end point validation that can be applied to summary data from published randomized trials. Methods: We performed a systematic review to identify studies describing meta-analytic statistical procedures to validate surrogate end points. Studies were eligible if the statistical procedure being described could be applied to summary estimates (such as risk ratios and mean differences) from published randomized controlled trials. We identified studies from comprehensive texts in the field of surrogate end point validation and an electronic search of MEDLINE (1930 - 2010). We evaluated the performance and reliability of the meta-analytic statistical procedures that we identified, by applying them to summary data from a previously published review of 11 randomized stent trials. We extracted summary data on the surrogate end point, in-segment diameter stenosis (% differences) at 6-9 months of follow-up, and the patient outcome target lesion revascularization (odds ratios) at 1 year of follow-up. Nine trials compared drug eluting stents with bare metal stents and 2 trials compared drug eluting stents. Results: In total, we identified 31 eligible articles that described 5 meta-analytic statistical procedures and indices to quantify the degree to which treatment effects on surrogate end points are predictive of their effects on patient outcomes. These include Spearman’s rank correlation coefficient (ρ), % concordance, R2, surrogate threshold effect (STE), and delta (Δ). The results of applying these procedures to summary data on in-segment diameter stenosis and target lesion revascularization were consistent with results from procedures requiring individual patient data and showed in-segment diameter stenosis is a good surrogate end point for target lesion revascularization. In 72.7% of studies (8 of 11), the effect of stenting on in-segment diameter stenosis agreed with its effect on target lesion revascularization (% concordance = 72.7%) and 85.2% of the variation in risk of target lesion revascularization was explained by in-segment diameter stenosis (R2=0.852, p <0.0001). A 17.7% difference in diameter stenosis is needed to predict a difference in risk of target lesion revascularization (STE=17.7%). These results agree with those from the ρ and Δ index. Conclusion: In conclusion, we have identified five meta-analytic statistical procedures which may be used to validate surrogate end points when individual patient data is unavailable.
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 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 | 0.359 | 0.692 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.068 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".