37 citations · 54 across the 9 of their papers we have counts for
9 papers
The Framework That Survives Bad Models: Human-AI Collaboration For Clinical Trials
Yao Chen, David Ohlssen, Aimee Readie +3
Artificial intelligence (AI) holds great promise for supporting clinical trials, from patient recruitment and endpoint assessment to treatment response prediction. However, deployi…
WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors
Konstantinos Sechidis, Sophie Sun, Yao Chen +8
This paper proposes a Workflow for Assessing Treatment effeCt Heterogeneity (WATCH) in clinical drug development targeted at clinical trial sponsors. WATCH is designed to address t…
TorchSurv: A Lightweight Package for Deep Survival Analysis
Mélodie Monod, Peter Krusche, Qian Cao +4
TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment. Unlike existing libraries that impose specific param…
Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation
Björn Bornkamp, Silvia Zaoli, Michela Azzarito +6
We present the motivation, experience and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challeng…
Principled Drug-Drug Interaction Terms for Bayesian Logistic Regression Models of Drug Safety in Oncology Phase I Combination Trials
Lukas A. Widmer, Andrew Bean, David Ohlssen +1
In Oncology, trials evaluating drug combinations are becoming more common. While combination therapies bring the potential for greater efficacy, they also create unique challenges…
A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
Hanxi Sun, Jason Plawinski, Sajanth Subramaniam +7
Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive pe…
Sequential knockoffs for continuous and categorical predictors: with application to a large Psoriatic Arthritis clinical trial pool
Matthias Kormaksson, Luke J. Kelly, Xuan Zhu +3
Knockoffs provide a general framework for controlling the false discovery rate when performing variable selection. Much of the Knockoffs literature focuses on theoretical challenge…
Conflict diagnostics for evidence synthesis in a multiple testing framework
Anne M. Presanis, David Ohlssen, Kai Cui +2
Evidence synthesis models that combine multiple datasets of varying design, to estimate quantities that cannot be directly observed, require the formulation of complex probabilisti…
Conflict Diagnostics in Directed Acyclic Graphs, with Applications in Bayesian Evidence Synthesis
Anne M. Presanis, David Ohlssen, David J. Spiegelhalter +1
Complex stochastic models represented by directed acyclic graphs (DAGs) are increasingly employed to synthesise multiple, imperfect and disparate sources of evidence, to estimate q…