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.
Highlights
Survival analysis is an important tool in a wide range of scientific fields, including toxicology [1,2,3,4], epidemiology [5, 6], pharmacology [7], medical research [6, 8,9,10], and biology [11,12,13]
In the engineering world survival analysis is known as reliability theory [14, 15] whereas in the social sciences it is termed event history analysis [16, 17]
We discuss the modelling of survival under chemical stress using General Unified Threshold model of Survival (GUTS) [4]
Summary
Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS). OPEN ACCESS Citation: Albert C, Vogel S, Ashauer R (2016) Computationally Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS). The General Unified Threshold model of Survival (GUTS) provides a consistent mathematical framework for survival analysis. We present a novel algorithm and its fast implementation in our R package, GUTS, that help to overcome these challenges. Data Availability Statement: All relevant data are within the paper, its Supporting Information files, and the software R package. This is a PLOS Computational Biology Software paper
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