Dose Finding Method in Joint Modeling of Efficacy and Safety Endpoints in Phase II Studies
Bibliographic record
Abstract
Determination of appropriate dose(s) to advance into Phase III trials is one of the most challenging and important tasks during drug development. Selecting a dose too high may result in unacceptable safety problems, while a too low dose may lead to ineffective drugs. Proper estimation of dose-response profiles for relevant safety and efficacy endpoints allows the reliable evaluation of the risk-benefit profile of a drug at the end of Phase II, as well as the selection of appropriate doses to be brought into confirmatory Phase III trials. Thus how to select dose(s) in Phase II trials by combining information about the efficacy and safety in a joint model setting may play a key role in drug development programs and can serve as a gate-keeper for large confirmatory Phase III trials with greater chance of success. Dose finding methods through joint modeling of both efficacy and safety endpoints are studied in this paper. To be more specific, we extend the popular MCP-Mod dose finding method (Bretz et al., 2005), which considered only the efficacy endpoint, to the method that incorporates both efficacy and safety endpoints through joint modeling. Method of parameter estimation for the extended models, and methods of selection of dose(s) to be brought into confirmatory Phase III trials based on Phase II study data are discussed in the paper. The performances of the proposed methods are evaluated through simulations.
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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".