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Image registration based automated lesion correspondence pipeline for longitudinal CT data

Autor: Mukherjee, Subrata, Coroller, Thibaud, Wang, Craig, Samala, Ravi K., Hu, Tingting, Gokcay, Didem, Petrick, Nicholas, Sahiner, Berkman, Cao, Qian

Patients diagnosed with metastatic breast cancer (mBC) typically undergo several radiographic assessments during their treatment. mBC often involves multiple metastatic lesions in different organs, it is imperative to accurately track and assess thes

Externí odkaz: http://arxiv.org/abs/2404.16544

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Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation

Autor: Ballerini, Veronica, Bornkamp, Björn, Mattei, Alessandra, Mealli, Fabrizia, Wang, Craig, Zhang, Yufen

In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization and, thus, challenging inference. Stakeholders in drug development are generally interested in going beyond the Intention-To-Treat (ITT) analysis,

Externí odkaz: http://arxiv.org/abs/2310.06653

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Combining Heterogeneous Spatial Datasets with Process-based Spatial Fusion Models: A Unifying Framework

Autor: Wang, Craig, Furrer, Reinhard

In modern spatial statistics, the structure of data that is collected has become more heterogeneous. Depending on the type of spatial data, different modeling strategies for spatial data are used. For example, a kriging approach for geostatistical da

Externí odkaz: http://arxiv.org/abs/1906.00364

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EggCounts: a Bayesian hierarchical toolkit to model faecal egg count reductions

Autor: Wang, Craig, Furrer, Reinhard

This is a vignette for the R package eggCounts version 2.0. The package implements a suite of Bayesian hierarchical models dealing with faecal egg count reductions. The models are designed for a variety of practical situations, including individual t

Externí odkaz: http://arxiv.org/abs/1804.11224

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Local-likelihood transformation kernel density estimation for positive random variables

Autor: Geenens, Gery, Wang, Craig

The kernel estimator is known not to be adequate for estimating the density of a positive random variable X. The main reason is the well-known boundary bias problems that it suffers from, but also its poor behaviour in the long right tail that such a

Externí odkaz: http://arxiv.org/abs/1602.04862

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