The Generalized 3+3 (G3+3) Design for Phase I Dose-Finding Trials
Ji, Y.; Zhang, Y.; Ji, A. L.
Show abstract
PURPOSEWe propose and demonstrate the feasibility and desirability of a novel model-free dose-finding design for phase I clinical trials. METHODSThe Generalized 3+3 (G3+3) design uses a set of simple rules summarized as follows: For 3 or 6 patients at a dose, apply the 3+3 design for making dosing decisions. For other numbers, if the observed toxicity rate (OTR) is less than 0.2, escalate to the next higher dose; if the OTR is greater than 0.29, de-escalate to the next lower dose; otherwise, stay at the current dose. RESULTSThe G3+3 design is the only design that can replicate the decisions of the 3+3 design for 3 or 6 patients among the popular designs compared like BOIN and i3+3. G3+3 generates desirable decisions when the number of patients treated is not 3 or 6, like the popular designs. Computer simulation verifies the superior operating characteristics of the G3+3 design. CONCLUSIONThe G3+3 design generalizes the popular 3+3 design so that desirable decisions can be made for any number of patients at a dose. G3+3 does not rely on statistical models, is simple and transparent, and can be implemented without a software tool. Therefore, it is expected to facilitate and enhance modern phase I dose-finding trials and early-phase drug development.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Controlled evaLuation of Angiotensin Receptor Blockers for COVID-19 respIraTorY disease (CLARITY): Statistical analysis plan for a randomised controlled Bayesian adaptive sample size trial 93%
- Graphing and reporting heterogeneous treatment effects through reference classes 91%
- Machine learning for randomised controlled trials: identifying treatment effect heterogeneity with strict control of type I error 90%
Similar papers in this journal
- Using numerical modelling and simulation to assess the ethical burden in clinical trials and how it relates to the proportion of responders in a trial sample 93%
- Applying Historical Data in a Nonlinear Mixed-Effects Model Can Reduce the Number of Control Rats Required for Calculation of the Relative Potency of Insulin Analogues 92%
- Analysis of clinical trial registry entry histories using the novel R package cthist 91%
Similar papers in this journal
- Modeling dynamic allocation of effort in a sequential task using discounting models 88%
- Implicit counterfactual effect in partial feedback reinforcement learning: behavioral and modeling approach 87%
- Artificial cerebellum on FPGA: Realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.