Population-adjusted indirect treatment comparison of the SOLO1 and PAOLA-1/ENGOT-ov25 trials evaluating maintenance olaparib or bevacizumab or the combination of both in newly diagnosed, advanced BRCA-mutated ovarian cancer
Bibliographic record
Abstract
BACKGROUND: In the absence of randomised head-to-head trials, we conducted a population-adjusted indirect treatment comparison (PA-ITC) of phase III trial data to evaluate the relative efficacy and safety of maintenance olaparib and bevacizumab alone and in combination in patients with newly diagnosed, advanced ovarian cancer and a BRCA mutation (BRCAm). METHODS: An unanchored PA-ITC was performed on investigator-assessed progression-free survival (PFS) data. Individual patient data from SOLO1 (olaparib versus placebo) and from BRCA-mutated patients in PAOLA-1/ENGOT-ov25 (olaparib plus bevacizumab versus placebo plus bevacizumab) were pooled. Each arm of PAOLA-1 was weighted so that key baseline patient characteristics were similar to the SOLO1 cohort. Analyses were performed in patients with complete baseline data. Weighted Cox regression analysis was used to estimate the comparative efficacy of different maintenance therapy strategies, supplemented by weighted Kaplan-Meier analyses. RESULTS: Data from SOLO1 patients (olaparib, n = 254; placebo, n = 126) were compared with data from BRCA-mutated PAOLA-1 patients (olaparib plus bevacizumab, n = 151; placebo plus bevacizumab, n = 71). Adding bevacizumab to olaparib was associated with a numerical improvement in PFS compared with olaparib alone (hazard ratio [HR] 0.71; 95% confidence interval [CI] 0.45-1.09). Statistically significant improvements in PFS were seen with olaparib alone versus placebo plus bevacizumab (HR 0.48; 95% CI 0.30-0.75), olaparib plus bevacizumab versus placebo (0.23; 0.14-0.34), and placebo plus bevacizumab versus placebo (0.65; 0.43-0.95). CONCLUSIONS: Results of this hypothesis-generating PA-ITC analysis support the use of maintenance olaparib alone or with bevacizumab in patients with newly diagnosed, advanced ovarian cancer and a BRCAm.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".