Joint Modelling of a Binary and a Continuous Outcome Measured at Two Cycles to Determine the Optimal Dose
Bibliographic record
Abstract
Summary The optimal dose of targeted treatment in oncology may not be the maximal tolerated dose. Evaluating jointly toxicity and efficacy data is then desirable. We propose an adaptive dose finding approach to identify a dose based on repeated binary toxicity and continuous efficacy outcomes from the first two cycles. Probit and linear Gaussian models are used for the toxicity and efficacy at each cycle respectively. The correlation between toxicity and efficacy outcome is modelled via a latent Gaussian variable. Maximum likelihood estimators are used. Two steps in this design are defined: dose escalation with decision rules based only on toxicity observed at the first cycle; the expansion cohort with decision rules based on both repeated toxicity and efficacy outcomes by using the joint model. We perform simulation studies to assess the operating characteristics of our design. The design has good performance for different scenarios. The percentage of correct selection dose varies from 54% to 84%. There is no effect on the estimation parameters with missing data of toxicity or efficacy at cycle 2. The design then has similar performance. Using repeated toxicity and efficacy data in dose finding trials provides more reliable information to estimate the optimal dose for further trials.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".