Speaker
Description
The Bayesian Logistic Regression Model (BLRM) with Escalation With Overdose Control (EWOC) is widely used in Phase I Oncology trials. Recently, several publications have highlighted a recurring issue: escalation can be blocked even when observed data strongly suggest safety. I.e., the posterior overdose probability at the next dose remains above the EWOC threshold despite no dose-limiting toxicities at the current dose or below. In addition, early extremes, such as one dose-limiting toxicity (DLT) event in a one-patient cohort, may have disproportionate impact on trial operating characteristics.
These issues highlight the importance of an adequate choice of prior for dose escalation with the BLRM method. The starting dose and the planned escalation grid already encode a strong, asymmetric belief that implies a prior on the dose-DLT response curve: the starting dose is chosen such that it is likely to be safe, whereas uncertainty grows towards higher dose levels.
We propose multivariate priors on the log-intercept and log-slope that make the implicit design prior explicit. The prior’s location / reference dose, variance and correlation are calibrated by prior predictive checks against a library of “reasonable” escalation paths (e.g., repeated 0 out of 3 DLT events should allow further for escalation, an early single DLT should not stop the trial for toxicity) that reflect the team’s expectations given the chosen starting dose and grid. Calibration targets include: escalation coherence when current data are safe, avoiding overreaction to single early DLT events, and early decisions qualitatively aligned with 3+3 in the first cohort.
The design uses standard EWOC (no relaxation of the feasibility bound) and over dose interval probabilities. While the approach is deliberately non mixture for simplicity and interpretability, it admits a straightforward extension to mixtures if additional flexibility is desired.
We simulated canonical dose-toxicity scenarios across shallow/steep curves, low/high MTDs, and early extreme outcomes. With the calibrated prior and unchanged EWOC, we observed: elimination of safe data lock, robustness to early DLTs without stalling and improved compatibility with 3+3 early on, while maintaining control of overdose allocation and overdose MTD declaration.
By aligning BLRM priors with the implicit beliefs induced by the starting dose and grid, we can prevent unnecessary stalls and preserve patient safety under standard EWOC while retaining simplicity of implementation (e.g., via the OncoBayes2 R package). Our priors offer a pragmatic upgrade for users seeking reliability in the opening moves in absence of historical data while maintaining rigor later.
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