246 citations · 274 across the 20 of their papers we have counts for
22 papers
Days Alive at Home vs Out of Hospital: Why the Difference Matters for Trial Endpoints
Letao Yuan, Sarah N. Dawson, David S. Robertson +5
Days Alive and Out of Hospital (DAOH) and Days Alive at Home (DAH) are increasingly used as patient-centred primary outcomes in perioperative trials, and a recent editorial has adv…
Introducing precision-weighted bias as a performance measure to inform the inclusion of adaptive designs in meta-analysis
Martin Law, David S. Robertson, Sofia S. Villar +4
We propose a novel, intuitive measure of statistical performance: precision-weighted bias. Precision-weighted bias is defined as the unconditional bias of an estimator weighted by…
Beyond the Composite: Enhancing Trial Analysis through a Divide & Conquer Approach to 'Days Alive and at Home': Insights from the NOTACS trial
Letao Yuan, Sofía S. Villar, Dominique-Laurent Couturier
"Days alive and at home" (DAH) is a recent patient-centered outcome measure for perioperative trials, defined as the number of days a patient spends at home during the follow-up pe…
Component over Composite: Mitigating Type I Error Inflation when Imputing "Days Alive and at Home"
Mia S. Tackney, Sarah Dawson, Letao Yuan +2
Background: Days Alive and at Home (DAH) over a pre-defined follow-up period is a novel post-intervention composite outcome that combines data from at least three components: (i) i…
Operationalizing Allocation Probability Tests: Practical Guidance on Optimized Implementation for Power and Robustness
Stina Zetterstrom, David S. Robertson, Thomas Jaki +1
Recently, a new testing approach for response-adaptive clinical trials was proposed based on the allocation probabilities (AP) rather than the outcome data. While original work on…
Capturing the Curve: Functional Data Analysis for Validated Digital Outcome Measures
Mia S. Tackney, Marcos Matabuena, Marco Palma +7
Digital health technologies enable high-frequency collection of data in near-continuous time and capture rich information about the health of individuals. The raw data collected by…
Exact statistical analysis for response-adaptive clinical trials: A general and computationally tractable approach
Stef Baas, Peter Jacko, Sofía S. Villar
Response-adaptive clinical trial designs allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. Response-adaptive des…
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Harsh Kumar, Tong Li, Jiakai Shi +11
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these intervention…
Computing the Performance of A New Adaptive Sampling Algorithm Based on The Gittins Index in Experiments with Exponential Rewards
James K. He, Sofía S. Villar, Lida Mavrogonatou
Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal a…
Multi-disciplinary fairness considerations in machine learning for clinical trials
Isabel Chien, Nina Deliu, Richard E. Turner +3
While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A n…
Some performance considerations when using multi-armed bandit algorithms in the presence of missing data
Xijin Chen, Kim May Lee, Sofia S. Villar +1
When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where t…
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization
Tong Li, Jacob Nogas, Haochen Song +8
Traditional randomized A/B experiments assign arms with uniform random (UR) probability, such as 50/50 assignment to two versions of a website to discover whether one version engag…
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling
Nina Deliu, Joseph J. Williams, Sofia S. Villar
Using bandit algorithms to conduct adaptive randomised experiments can minimise regret, but it poses major challenges for statistical inference (e.g., biased estimators, inflated t…
Quantifying efficiency gains of innovative designs of two-arm vaccine trials for COVID-19 using an epidemic simulation model
Rob Johnson, Chris Jackson, Anne Presanis +2
Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect th…
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
Joseph Jay Williams, Jacob Nogas, Nina Deliu +4
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative in…
A Novel Statistical Test for Treatment Differences in Clinical Trials using a Response Adaptive Forward Looking Gittins Index Rule
Helen Yvette Barnett, Sofia S Villar, Helena Geys +1
The most common objective for response adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by al…
Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs
Thomas Burnett, Pavel Mozgunov, Philip Pallmann +3
Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical and efficient. These benefits are…
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Cong Shen, Zhiyang Wang, Sofia S. Villar +1
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite t…
Response-adaptive randomization in clinical trials: from myths to practical considerations
David S. Robertson, Kim May Lee, Boryana C. Lopez-Kolkovska +1
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In t…
Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints
Adam Smith, Sofia S. Villar
Adaptive designs for multi-armed clinical trials have become increasingly popular recently in many areas of medical research because of their potential to shorten development times…
Response adaptive designs for binary responses: how to offer patient benefit while being robust to time trends?
Sofia S. Villar, Jack Bowden, James Wason
Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability…
Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges
Sofía S. Villar, Jack Bowden, James Wason
Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since th…