• Corpus ID: 246823695

Incentivizing Participation in Clinical Trials

  title={Incentivizing Participation in Clinical Trials},
  author={Yingkai Li and Aleksandrs Slivkins},
The difficulty of recruiting patients is a well-known issue in clinical trials which inhibits or sometimes precludes them in practice. We incentivize participation in clinical trials by leveraging information asymmetry between the trial and the patients. We obtain an optimal solution in terms of the statistical performance of the trial, as expressed by an estimation error. Namely, we provide an incentive-compatible mechanism with a particular guarantee, and a nearly matching impossibility… 



Monitoring Clinical Trials with Multiple Arms

The main focus of the article is on the application and extension of (adaptive) closed testing procedures in the group sequential setting that strongly control the familywise error rate.

Incentivizing Exploration with Selective Data Disclosure

This is the first paper in the literature on incentivized exploration (and possibly in the broader literature on "learning and incentives") which attempts to mitigate the limitations of standard economic assumptions.

Implementing the “Wisdom of the Crowd”

The optimal disclosure policy of a planner whose goal is to maximizes social welfare is characterized, which is the implementation of what is known as the 'wisdom of the crowd'.

Doubly Robust Policy Evaluation and Optimization

It is proved that the doubly robust estimation method uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies, and is expected to become common practice in policy evaluation and optimization.

Multi-Arm Clinical Trials of New Agents: Some Design Considerations

This work presents a rationale for not requiring multiplicity adjustment in multi-arm trials that are designed for logistical efficiency, and shows that this multi- arm design is shown to require a lower total sample size than multiple two- arm trials.

Bayesian Incentive-Compatible Bandit Exploration

A black-box reduction from an arbitrary multi-arm bandit algorithm to an incentive-compatible one, with only a constant multiplicative increase in regret is provided, which works for very general bandit settings, even ones that incorporate contexts and arbitrary partial feedback.

Adaptive design methods in clinical trials – a review

Several commonly considered adaptive designs in clinical trials are reviewed and some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given.

More multiarm randomised trials of superiority are needed

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits

The SWITCH estimator is proposed, which can use an existing reward model to achieve a better bias-variance tradeoff than IPS and DR and prove an upper bound on its MSE and demonstrate its benefits empirically on a diverse collection of data sets, often outperforming prior work by orders of magnitude.

Bayesian Persuasion and Information Design

A school may improve its students’ job outcomes if it issues only coarse grades. Google can reduce congestion on roads by giving drivers noisy information about the state of traffic. A social planner