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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,982 papers · 148 categories

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0111 · Mar 201919922001200920172026
7 results for “Dose-Finding”
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trialsstat.ML

Study uses Thompson Sampling to find optimal drug dosages in clinical trials.

problem Finding the best drug dosage in early-stage clinical trials.
method Adopted Thompson Sampling for dose-finding clinical trials, considering different monotonicity assumptions.
result Thompson Sampling variants outperform existing methods in various dose-finding scenarios.
Thompson SamplingClinical TrialsDose-FindingMonotonicityPhase I/II Trials
2019-03-17abs ↗pdf ↗
Dose-finding design based on level set estimation in phase I cancer clinical trialsstat.ML

A novel dose-finding design for cancer clinical trials using level set estimation.

problem Finding the maximum tolerated dose (MTD) in phase I cancer clinical trials.
method Proposes a novel dose-finding design based on level set estimation (LSE) to determine the next dose.
result The proposed LSE design achieves higher accuracy in estimating the MTD and lower risk of overdosing compared to existing designs.
phase I cancer clinical trialsmaximum tolerated dose (MTD)level set estimation (LSE)dose-finding designoverdose control
2025-04-12abs ↗pdf ↗
Contextual Constrained Learning for Dose-Finding Clinical Trialsstat.ML

C3T-Budget optimizes drug efficacy in dose-finding trials with budget and safety constraints.

problem Heterogeneous patient populations and budget constraints make dose-finding clinical trials challenging.
method Contextual constrained clinical trial algorithm that maximizes drug efficacy while learning subgroup responses.
result Demonstrates efficient budget usage and balanced learning-treatment trade-off in simulated trials.
clinical trialsdose-findingbudget constraintssubgroup analysisdrug efficacy
2020-01-08abs ↗pdf ↗
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settingsstat.ML

Develops deep jump learning for continuous treatment OPE.

problem Estimating mean outcomes under new treatment rules using historical data from different rules.
method Adaptive deep discretization of continuous treatment space using deep learning and multi-scale change point detection.
result Validated method through theoretical results, simulations, and real application to Warfarin Dosing.
Deep Jump LearningOff-Policy EvaluationContinuous TreatmentPersonalized Dose-FindingMulti-Scale Change Point Detection
2020-10-29abs ↗pdf ↗
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraintscs.LG

SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.

problem Complex relationships between efficacy and toxicity in new drug trials.
method Adaptive clinical trial methodology that maximizes cumulative efficacy while ensuring safety constraints.
result SEEDA outperforms existing methods in finding optimal doses with higher success rates and fewer patients.
clinical trialsdose allocationadaptive methodscumulative efficacytoxicity safety
2020-06-09abs ↗pdf ↗
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groupscs.LG

SDF-Bayes finds safe drug combinations safely, balancing optimism and caution.

problem Finding safe drug combinations in clinical trials with multiple drugs and patient heterogeneity.
method SDF-Bayes uses Bayesian statistics to choose the most likely MTD while ensuring safety constraints.
result SDF-Bayes outperforms existing methods in both accuracy and safety for drug combination trials.
Bayesian designdrug combinationsclinical trialssafety constraintspatient heterogeneity
2021-01-26abs ↗pdf ↗