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Computationally Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS).

Albert C, Vogel S, Ashauer R - PLoS Comput. Biol. (2016)

Bottom Line: Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user.We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences.In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods.

View Article: PubMed Central - PubMed

Affiliation: Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.

ABSTRACT
The General Unified Threshold model of Survival (GUTS) provides a consistent mathematical framework for survival analysis. However, the calibration of GUTS models is computationally challenging. We present a novel algorithm and its fast implementation in our R package, GUTS, that help to overcome these challenges. We show a step-by-step application example consisting of model calibration and uncertainty estimation as well as making probabilistic predictions and validating the model with new data. Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user. The complete ready-to-run script is available as supplemental material. We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences. In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods.

No MeSH data available.


Chains of the parameters’ posterior marginals computed by the MCMC.
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pcbi.1004978.g001: Chains of the parameters’ posterior marginals computed by the MCMC.

Mentions: After the MCMC has finished without errors, it is necessary to inspect the chains and check whether they have converged. Automatised checks are available (e.g., through using CODA [37]), however, here we create a plot from the chains of the parameters’ posterior marginals and check the chains visually (see Fig 1).


Computationally Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS).

Albert C, Vogel S, Ashauer R - PLoS Comput. Biol. (2016)

Chains of the parameters’ posterior marginals computed by the MCMC.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920405&req=5

pcbi.1004978.g001: Chains of the parameters’ posterior marginals computed by the MCMC.
Mentions: After the MCMC has finished without errors, it is necessary to inspect the chains and check whether they have converged. Automatised checks are available (e.g., through using CODA [37]), however, here we create a plot from the chains of the parameters’ posterior marginals and check the chains visually (see Fig 1).

Bottom Line: Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user.We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences.In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods.

View Article: PubMed Central - PubMed

Affiliation: Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.

ABSTRACT
The General Unified Threshold model of Survival (GUTS) provides a consistent mathematical framework for survival analysis. However, the calibration of GUTS models is computationally challenging. We present a novel algorithm and its fast implementation in our R package, GUTS, that help to overcome these challenges. We show a step-by-step application example consisting of model calibration and uncertainty estimation as well as making probabilistic predictions and validating the model with new data. Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user. The complete ready-to-run script is available as supplemental material. We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences. In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods.

No MeSH data available.