Recent Posts

I received a question the other day by email regarding the parameterization of the Weibull survival model which is included in both SurvivalStan (a Python package) and biostan, an R mini-package I put together for a BioConductor workshop held in 2016. The question read: Hi Jacki, I wonder if you might be able to help me with a question? I have been looking at stan recently for survival models and I noticed the biostan repository, which is quite a helpful resource.

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Survival analysis is an important and useful tool in biostatistics. It is commonly used in the analysis of clinical trial data, where the time to a clinical event is a primary endpoint. This endpoint may or may not be observed for all patients during the study’s follow-up period. At the core of survival analysis is the observation that the observed time to event $t$ is the result of an accumulation of event-related risks at each moment up to that time $t$.

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Recent Publications

More Publications

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

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Correlation of degree of tumor immune infiltration and insertion-and-deletion (indel) burden with outcome on programmed death 1 (PD1) therapy in advanced renal cell cancer (RCC).

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Joint longitudinal and time-to-event models for multilevel hierarchical data

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Infino: a Bayesian hierarchical model improves estimates of immune infiltration into tumor microenvironment

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Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis

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$delta$-Catenin Is Genetically and Biologically Associated with Cortical Cataract and Future Alzheimer-Related Structural and Functional Brain Changes

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Talks

Tutorial on survival analysis using Stan

This workshop will provide a background and introduction to Bayesian analysis using Stan and R interfaces to Stan.

Contact

  • jackinovik@gmail.com
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