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. 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.

[1]  Roman Ashauer,et al.  Advantages of toxicokinetic and toxicodynamic modelling in aquatic ecotoxicology and risk assessment. , 2010, Journal of environmental monitoring : JEM.

[2]  Steve Selvin.  Survival Analysis for Epidemiologic and Medical Research: Survival Analysis for Epidemiologic and Medical Research , 2008 .

[3]  G. Guo.  Event-history analysis for left-truncated data. , 1993, Sociological methodology.

[4]  Roman Ashauer,et al.  Death Dilemma and Organism Recovery in Ecotoxicology. , 2015, Environmental science & technology.

[5]  Rémy Beaudouin,et al.  Individual sensitivity distribution evaluation from survival data using a mechanistic model: implications for ecotoxicological risk assessment. , 2012, Chemosphere.

[6]  Roman Ashauer,et al.  Toxicokinetic-toxicodynamic modelling of survival of Gammarus pulex in multiple pulse exposures to propiconazole: model assumptions, calibration data requirements and predictive power , 2012, Ecotoxicology.

[7]  S. Jay Olshansky,et al.  Mortality Partitions and their Relevance to Research on Senescence , 2006, Biogerontology.

[8]  Roman Ashauer,et al.  The Insecticide Imidacloprid Causes Mortality of the Freshwater Amphipod Gammarus pulex by Interfering with Feeding Behavior , 2013, PloS one.

[9]  Benjamin Daniels,et al.  Life-stage-dependent sensitivity of the cyclopoid copepod Mesocyclops leuckarti to triphenyltin. , 2013, Chemosphere.

[10]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[11]  N Cascinelli,et al.  Prognostic factors analysis of 17,600 melanoma patients: validation of the American Joint Committee on Cancer melanoma staging system. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  R. Ferrari,et al.  Chromogranin A in heart failure; a novel neurohumoral factor and a predictor for mortality. , 2002, European heart journal.

[13]  Steve Selvin,et al.  Survival Analysis for Epidemiologic and Medical Research , 2008 .

[14]  Roman Ashauer,et al.  General unified threshold model of survival--a toxicokinetic-toxicodynamic framework for ecotoxicology. , 2011, Environmental science & technology.

[15]  N. Keiding,et al.  Multi-state models for event history analysis , 2002, Statistical methods in medical research.

[16]  L. Madden,et al.  New applications of statistical tools in plant pathology. , 2004, Phytopathology.

[17]  R. Collins,et al.  The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials , 2012, The Lancet.

[18]  Dirk Eddelbuettel,et al.  Rcpp: Seamless R and C++ Integration , 2011 .

[19]  L. Gavrilov,et al.  The reliability theory of aging and longevity. , 2001, Journal of theoretical biology.

[20]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[21]  André Gergs,et al.  Body size-mediated starvation resistance in an insect predator. , 2014, The Journal of animal ecology.

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  C. I. Bliss,et al.  THE METHOD OF PROBITS. , 1934, Science.

[24]  Martin A. Hamilton,et al.  Toxicity Curve Estimation: Fitting a Compartment Model to Median Survival Times , 1985 .

[25]  M. C. Newman,et al.  Fundamentals of Ecotoxicology , 2019 .

[26]  Niels Keiding,et al.  Event history analysis and the cross‐section , 2006, Statistics in medicine.

[27]  Sebastiaan A L M Kooijman,et al.  Making Sense of Ecotoxicological Test Results: Towards Application of Process-based Models , 2006, Ecotoxicology.

[28]  William J. Kolarik,et al.  Real-time performance reliability prediction , 2001, IEEE Trans. Reliab..

[29]  Niels Keiding,et al.  Statistical Models Based on Counting Processes , 1993 .

[30]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[31]  Steve Selvin.  Survival Analysis for Epidemiologic and Medical Research: Contents , 2008 .

[32]  Roman Ashauer,et al.  A METHOD TO PREDICT AND UNDERSTAND FISH SURVIVAL UNDER DYNAMIC CHEMICAL STRESS USING STANDARD ECOTOXICITY DATA , 2013, Environmental toxicology and chemistry.

[33]  Christopher Zorn,et al.  Nonproportional Hazards and Event History Analysis in International Relations , 2003 .

[34]  Cholesterol Treatment Trialists' Collaborato.  The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials , 2012 .

[35]  Dirk Eddelbuettel,et al.  Seamless R and C++ Integration with Rcpp , 2013 .

[36]  Hans R. Künsch,et al.  Bayesian experimental design for a toxicokinetic–toxicodynamic model , 2012 .

[37]  Roman Ashauer,et al.  Toxicokinetic and toxicodynamic modeling explains carry-over toxicity from exposure to diazinon by slow organism recovery. , 2010, Environmental science & technology.