6 citations · 8 across the 11 of their papers we have counts for
12 papers
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…
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…
Robust Bayesian Sequential Borrowing for Multi-Population Clinical Programmes
Erik Hermansson, Lynn Dunsire, David Svensson +1
We introduce Robust Bayesian Sequential Borrowing (RBSB), a framework for extrapolating evidence across adjacent subgroups in multi-population clinical programmes where studies are…
Evaluating Predictive Modeling Strategies for Predicting Individual Treatment Effects in Precision Medicine
Pamela M. Chiroque-Solano, M Lee Van Horn, Thomas Jaki
Precision medicine seeks to match patients with treatments that produce the greatest benefit. The Predicted Individual Treatment Effect (PITE)-the difference between predicted outc…
Optimal weighted tests for replication studies and the two-trials rule
David S. Robertson, Thomas Jaki
Replication studies for scientific research are an important part of ensuring the reliability and integrity of experimental findings. In the context of clinical trials, the concept…
A comparison of approaches to incorporate patient-selected and patient-ranked outcomes in clinical trials
David S. Robertson, Thomas Jaki
A key aspect of patient-focused drug development is identifying and measuring outcomes that are important to patients in clinical trials. Many medical conditions affect multiple sy…
A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials
Pamela Solano, M Lee Van Horn, Kyle Walters +4
Alcohol Use Disorder (AUD) treatment presents high individual-level heterogeneity, with outcomes ranging from complete abstinence to persistent heavy drinking. This variability-dri…
Joint TITE-CRM for Dual Agent Dose Finding Studies
Helen Barnett, Oliver Boix, Dimitris Kontos +1
Dual agent dose-finding trials study the effect of a combination of more than one agent, where the objective is to find the Maximum Tolerated Dose Combination (MTC), the combinatio…
Confidence intervals for adaptive trial designs II: Case study and practical guidance
David S. Robertson, Thomas Burnett, Babak Choodari-Oskooei +4
In adaptive clinical trials, the conventional confidence interval (CI) for a treatment effect is prone to undesirable properties such as undercoverage and potential inconsistency w…
Confidence intervals for adaptive trial designs I: A methodological review
David S. Robertson, Thomas Burnett, Babak Choodari-Oskooei +4
Regulatory guidance notes the need for caution in the interpretation of confidence intervals (CIs) constructed during and after an adaptive clinical trial. Conventional CIs of the…
A fast, flexible simulation framework for Bayesian adaptive designs -- the R package BATSS
Dominique-Laurent Couturier, Rainer Puhr, Stephane Heritier +2
The use of Bayesian adaptive designs for randomised controlled trials has been hindered by the lack of software readily available to statisticians. We have developed a new software…
Improving Software Engineering in Biostatistics: Challenges and Opportunities
Daniel Sabanés Bové, Heidi Seibold, Anne-Laure Boulesteix +9
Programming is ubiquitous in applied biostatistics; adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key c…