65 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of TorontoCA14 papers
- Hospital for Sick ChildrenCA8 papers
- Institute for Clinical Evaluative SciencesCA6 papers
- SickKids FoundationCA6 papers
- University College LondonGB6 papers
- Bayer (United Kingdom)GB2 papers
- Health Economics and Outcomes Research (United Kingdom)GB2 papers
- University Health NetworkCA2 papers
- Alberta Bone and Joint Health InstituteCA1 paper
- Canadian Cancer SocietyCA1 paper
- Canadian Institute for Advanced ResearchCA1 paper
- Centre Hospitalier de l’Université de MontréalCA1 paper
17 papers
MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation
Kourosh Darvish, Arjun Sohal, Abhijoy Mandal +20
Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and thi…
Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
Lisa Pilgram, Kai Yang, Ana-Alicia Beltran-Bless +22
Prognostic information is essential for decision-making in breast cancer management. Recently trials have predominantly focused on genomic prognostication tools, even though clinic…
Stable CDE Autoencoders with Acuity Regularization for Offline Reinforcement Learning in Sepsis Treatment
Yue Gao
Effective reinforcement learning (RL) for sepsis treatment depends on learning stable, clinically meaningful state representations from irregular ICU time series. While previous wo…
Bayesian Sensitivity Analysis for Causal Estimation with Time-varying Unmeasured Confounding
Yushu Zou, Liangyuan Hu, Amanda Ricciuto +2
Causal inference relies on the untestable assumption of no unmeasured confounding. Sensitivity analysis can be used to quantify the impact of unmeasured confounding on causal estim…
Effect modification and non-collapsibility leads to conflicting treatment decisions: a review of marginal and conditional estimands and recommendations for decision-making
David M. Phillippo, Antonio Remiro-Azócar, Anna Heath +4
Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, t…
ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing
Michael T. Kuczynski, Nathan J. Neeteson, Kathryn S. Stok +9
High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique capable of imaging trabecular bone in-vivo. HR-pQCT has a wide range of applications,…
Methodological considerations for novel approaches to covariate-adjusted indirect treatment comparisons
Antonio Remiro-Azócar, Anna Heath, Gianluca Baio
We examine four important considerations in the development of covariate adjustment methodologies for indirect treatment comparisons. Firstly, we consider potential advantages of w…
Model-based standardization using multiple imputation
Antonio Remiro-Azócar, Anna Heath, Gianluca Baio
When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariat…
Parametric G-computation for Compatible Indirect Treatment Comparisons with Limited Individual Patient Data
Antonio Remiro-Azócar, Anna Heath, Gianluca Baio
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial difference…
A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT
Hojjat Salehinejad, Jumpei Kitamura, Noah Ditkofsky +7
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, thes…
Conflating marginal and conditional treatment effects: Comments on 'Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study'
Antonio Remiro-Azócar, Anna Heath, Gianluca Baio
In this commentary, we highlight the importance of: (1) carefully considering and clarifying whether a marginal or conditional treatment effect is of interest in a population-adjus…
Effect modification in anchored indirect treatment comparisons: Comments on "Matching-adjusted indirect comparisons: Application to time-to-event data"
Antonio Remiro-Azócar, Anna Heath, Gianluca Baio
This commentary regards a recent simulation study conducted by Aouni, Gaudel-Dedieu and Sebastien, evaluating the performance of different versions of matching-adjusted indirect co…
Causal mediation analysis decomposition of between-hospital variance
Bo Chen, Keith A. Lawson, Antonio Finelli +1
Causal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While v…
A Bayes Factor Approach with Informative Prior for Rare Genetic Variant Analysis from Next Generation Sequencing Data
Jingxiong Xu, Wei Xu, Laurent Briollais
The discovery of rare genetic variants through Next Generation Sequencing is a very challenging issue in the field of human genetics. We propose a novel region-based statistical ap…
A Bayesian view of doubly robust causal inference
Olli Saarela, Léo R. Belzile, David A. Stephens
In causal inference confounding may be controlled either through regression adjustment in an outcome model, or through propensity score adjustment or inverse probability of treatme…
A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings
Nan Wang, Blesson Varghese, Peter D. Donnelly
Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and…
A Generalized Levene's Scale Test for Variance Heterogeneity in the Presence of Sample Correlation and Group Uncertainty
David Soave, Lei Sun
We generalize Levene's test for variance (scale) heterogeneity between groups for more complex data, which includes sample correlation and group membership uncertainty. Followi…