We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest…
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48 results for “Multi-arm trials”
Proposes an interpretable machine learning framework for multi-arm HTE estimation.
problem Challenges in estimating heterogeneous treatment effects in multi-arm settings.
method Rule-based ensemble approach for HTE estimation in multi-arm trials.
result Achieved lower bias and higher estimation accuracy compared to existing methods.
Logarithmic regret strategies for safe multi-armed bandits with safety risk constraints.
problem Maximizing reward while avoiding unsafe arms under safety risk constraints.
method Doubly optimistic strategies with pseudo-regret formulation.
result Logarithmic regret bounds for safe multi-armed bandits.
The paper tackles online learning problems with monotone arm sequences, achieving optimal or near-optimal regret bounds.
problem Online learning problems with ordinal and monotone arm sequences, such as dynamic pricing and clinical trials.
method Proposes algorithms for continuum-armed bandit problems with monotone arm sequences, achieving optimal or near-optimal regret bounds.
result Achieves optimal or near-optimal regret bounds for monotone arm sequences, differing from the continuous-armed bandit literature.
New method for contextual bandits with corrupted context.
problem Contextual bandits with corrupted context in online settings.
method Combining contextual bandit and multi-armed bandit approaches.
result Improved learning from all iterations, including corrupted ones.
Study examines impact of missing data on multi-armed bandit algorithms.
problem Impact of missing data on performance of multi-armed bandit algorithms.
method Extensive simulation study of two-armed bandit algorithms with binary outcomes, considering different probabilities of missingness.
result Impact on performance varies depending on the balance between exploration and exploitation.
New algorithm tackles resource allocation in multi-armed bandits to balance speed and throughput.
problem Balancing speed and throughput in stochastic multi-armed bandits with limited resources.
method Proposes an algorithm that trades off between information accumulation and throughput.
result Upper bounds the time taken to find the best arm with a given target success probability.
MAYA learns bee foraging decisions with limited memory.
problem Reproducing and predicting bees' foraging decisions with limited memory.
method Sequential imitation learning model based on multi-armed bandits, considering a temporal window τ of 7 trials.
result MAYA outperforms imitation baselines and classical models, providing interpretability and realistic trajectories.
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommende…
We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. This is a problem for applications such as adaptive clinical trials, experiment design, and user-targeted advertising where private information is connected to individual rewards. Our major contribution is to show that the…
New algorithms identify best arm with less pulls, adapting to arm covariances.
problem Best arm identification under dependent and correlated arm distributions.
method Adaptive algorithms estimating arm covariances to minimize pulls.
result Substantial improvement in best arm identification over standard setting.
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trialscs.LG
The stochastic multi-armed bandit problem is a well-known model for studying the exploration-exploitation trade-off. It has significant possible applications in adaptive clinical trials, which allow for dynamic changes in the treatment allocation probabilities of patients. However, most bandit learning algorithms are d…
Survey on risk-aware multi-armed bandits for better decision-making.
problem Risk measures in multi-armed bandits for better decision-making.
method Review of existing research, definition of risk-aware bandit problems, and algorithms for minimizing regret and identifying best arms.
result Consolidation and summarization of existing research on risk measures in multi-armed bandits.
Paper tackles robust batched bandits for heavy-tailed rewards.
problem Clinical trials and other applications with heavy-tailed rewards.
method Proposes robust batched bandit algorithms for heavy-tailed rewards in finite-arm and Lipschitz-continuous settings.
result Heavier-tailed rewards require fewer batches for near-optimal regret in the instance-independent regime and Lipschitz setting.
Safe actions learned in finite trials, without infinite exploration.
problem Learning safe actions in unknown environments efficiently.
method Defining a handicap metric and using sequential probability ratio test for discarding unsafe actions.
result Achieves constant handicap, discarding unsafe machines with probability one in finite rounds.
New algorithms improve performance guarantees for multi-armed bandits problems.
problem Allocating effort under uncertainty in scenarios like investing research effort.
method Proposed two new families of bandit algorithms with stronger guarantees.
result Achieved optimal dependence on k with additional properties of arm reward curves.
Efficiently identifies best algorithms for game tasks.
problem Selecting optimal algorithms for game tasks efficiently.
method Best arm identification for multi-armed bandits with confidence intervals.
result Significantly improved performance in simple regret and error probability.
Instrument-Armed Banditsstat.ML
We extend the classic multi-armed bandit (MAB) model to the setting of noncompliance, where the arm pull is a mere instrument and the treatment applied may differ from it, which gives rise to the instrument-armed bandit (IAB) problem. The IAB setting is relevant whenever the experimental units are human since free will…
Over the past decade, contextual bandit algorithms have been gaining in popularity due to their effectiveness and flexibility in solving sequential decision problems---from online advertising and finance to clinical trial design and personalized medicine. At the same time, there are, as of yet, surprisingly few options…
New algorithm minimizes regret in multi-armed bandits with network interference.
problem Minimizing regret in online experiments with network interference.
method Sparse network interference model, discrete Fourier analysis, linear regression-based algorithms.
result Provable low regret algorithms for sparse interference networks.
New algorithms find all ε-good arms in stochastic bandits.
problem Finding all arms with means above a specified threshold in stochastic bandits.
method Two algorithms introduced to identify all ε-good arms.
result Demonstrated great empirical performance on large datasets.
New method for contextual bandit with missing rewards.
problem Contextual bandit with missing rewards in online settings.
method Combining contextual bandit approach with unsupervised learning (clustering) to estimate missing rewards.
result Promising empirical results on real-life datasets.
A new method for adaptive experiments improves inference.
problem Current inference methods for adaptive experiments are weak and asymptotic.
method Simulation-based inference using optimistic simulations.
result Our method achieves better coverage and narrower intervals.
Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge th…
Machine learning predicts patient recruitment for clinical trials.
problem Improving patient recruitment prediction for clinical trials.
method Machine learning methods applied to historical clinical trial data.
result Reduced prediction error compared to current industry standards.
Improved single-pass streaming MAB regret bound to O(K^1/3T^2/3).
problem Minimize regret in single-pass streaming multi-armed bandits with sublinear memory.
method Improved regret lower bound to Ω(K^1/3T^2/3) and upper bound to O(K^1/3T^2/3).
result Achieved tight regret bound of O(K^1/3T^2/3) for algorithms with o(K) memory.
The (contextual) multi-armed bandit problem (MAB) provides a formalization of sequential decision-making which has many applications. However, validly evaluating MAB policies is challenging; we either resort to simulations which inherently include debatable assumptions, or we resort to expensive field trials. Recently …
Combines trial and observational data to improve policy evaluation.
problem External validity of randomized trial results in target populations.
method Uses covariate data to model trial sampling and certifies policy evaluations.
result Valid trial-based policy evaluations under model miscalibration.
MILCCI integrates labels across categories for better understanding of multi-trial data.
problem Understanding how labels encode multi-trial observations and disentangling their effects.
method Sparse per-trial decomposition leveraging label similarities within each category.
result MILCCI identifies interpretable components and integrates label information.
The paper compares methods for estimating heterogeneous treatment effects using multiple randomized trials.
problem Estimating heterogeneous treatment effects reliably and precisely with a single dataset is challenging.
method Non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials.
result Methods that directly allow for heterogeneity of the treatment effect across trials perform better than those that do not.
Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? Ho…
New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.
problem Determining the best allocation of resources in clinical trials.
method Analysis of batched arm elimination designs and comparison with completely randomized trials.
result Simple adaptive designs universally and strictly dominate non-adaptive completely randomized trials for at least three treatment arms.
New method detects biomarker-treatment interactions in clinical trials.
problem Detecting interactions between high-dimensional biomarkers and treatments in randomized trials.
method Two-stage penalized regression screening using ridge regression for multivariate screening.
result Ridge regression screening provides greater power than traditional methods in correlated data.
The personalization of treatment via bio-markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This paper explores the use of active learning techniques to desig…
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external datastat.ML
QR-learner estimates individual treatment effects using external data.
problem Limited power to detect individual treatment effects in randomized trials.
method Model-agnostic learner that estimates conditional average treatment effects (CATE) using external data.
result QR-learner reduces mean squared error and can recover true CATE.
Bayesian BIC for multi-trial data improves VAR model order selection.
problem Optimal VAR model order selection for multi-trial event-based data.
method Derive and apply Bayesian Information Criterion (BIC) for multi-trial ensemble data.
result Multi-trial BIC successfully recovers real model order and estimates small model order.
Optimum in Convex Hulls (OCH) generalizes clinical trial results to broader populations.
problem Clinical trials exclude confounding but limit recruitment; observational data are more inclusive but suffer from confounding.
method OCH uses convex hulls of conditional expectations or densities to approximate the true treatment effect from both observational and trial data.
result OCH estimates the treatment effect with state-of-the-art accuracy in terms of both expectations and densities.
Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failurestat.ML
Causal analysis reveals regional discrepancies in TOPCAT trial results.
problem Inconclusive results in TOPCAT trial for heart failure treatment.
method Causal discovery methods with domain knowledge integration.
result Significant causal effects shown for some subgroups globally.
Clinical trials in the medical domain are constrained by budgets. The number of patients that can be recruited is therefore limited. When a patient population is heterogeneous, this creates difficulties in learning subgroup specific responses to a particular drug and especially for a variety of dosages. In addition, pa…
The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.
problem Limited covariates in small clinical trials reduce the effectiveness of analysis.
method Leverage historical data to pre-specify covariate weights as a composite covariate.
result A composite covariate improves the cost/benefit ratio and reduces overfitting in small clinical trials.
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.
New methods improve subgroup analysis in trials with limited data.
problem Limited sample sizes in subgroup analyses of randomized controlled trials.
method Two TMLEs that borrow information from non-subgroup participants.
result Improved precision in subgroup-specific treatment effect estimates.
Syntax designs adaptive trials for subpopulations with potential benefits.
problem Identifying subpopulations with positive treatment effects in diverse patient populations.
method Adaptive patient recruitment and synthetic control estimation.
result Syntax outperforms conventional trial designs in identifying beneficial subpopulations.
Bayesian method improves clinical trial efficiency.
problem Increase treatment effect estimates in clinical trials.
method Combines prognostic covariate adjustment with a Bayesian framework.
result Substantial increase in statistical power with controlled type I error.
The P300 event-related potential (ERP), evoked in scalp-recorded electroencephalography (EEG) by external stimuli, has proven to be a reliable response for controlling a BCI. The P300 component of an event related potential is thus widely used in brain-computer interfaces to translate the subjects' intent by mere thoug…
The study optimizes free trial lengths to boost subscriptions and consumer loyalty.
problem Optimizing free trial lengths to maximize customer acquisition and retention.
method A large-scale field experiment with personalized policy design and evaluation.
result Personalized free trial policies outperform uniform trial lengths.
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutionsstat.ML
New framework for adaptive clinical trials to address real-world challenges.
problem Real-world challenges in post-regulatory clinical trials.
method RFAN framework integrating regulatory constraints and treatment policy value.
result Empirical evaluation of RFAN's performance.
Machine learning improves trial analysis precision by adjusting for prognostic variables.
problem Improving precision in randomized trial analyses using covariate adjustment.
method Targeted machine learning estimation (TMLE) with adaptive pre-specification.
result Maximized empirical efficiency through cross-validated variance minimization.