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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.


Correlations between the parameter posterior samples computed by the MCMC.
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pcbi.1004978.g002: Correlations between the parameter posterior samples computed by the MCMC.

Mentions: Next, we create a correlation plot, for the posterior parameter sample, computed by the MCMC. The parameters for our example co-vary as shown in Fig 2.


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)

Correlations between the parameter posterior samples computed by the MCMC.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004978.g002: Correlations between the parameter posterior samples computed by the MCMC.
Mentions: Next, we create a correlation plot, for the posterior parameter sample, computed by the MCMC. The parameters for our example co-vary as shown in Fig 2.

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.