Modelling grouped survival times in toxicological studies using generalized additive models
A method for combining a proportional-hazards survival time model with a bioassay model where the log-hazard function is modelled as a linear or smoothing spline function of log-concentration combined with a smoothing spline function of time is described. The combined model is fitted to mortality nu...
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ftdeakinunifig:oai:figshare.com:article/20917198 2024-06-23T07:46:57+00:00 Modelling grouped survival times in toxicological studies using generalized additive models SG Candy BJ Sfiligoj CK King Julie Mondon 2014-12-14T00:00:00Z http://hdl.handle.net/10536/DRO/DU:30069277 https://figshare.com/articles/journal_contribution/Modelling_grouped_survival_times_in_toxicological_studies_using_generalized_additive_models/20917198 unknown http://hdl.handle.net/10536/DRO/DU:30069277 https://figshare.com/articles/journal_contribution/Modelling_grouped_survival_times_in_toxicological_studies_using_generalized_additive_models/20917198 All Rights Reserved Dose–response model Generalized Additive Model Grouped survival times Time–response model 050204 Environmental Impact Assessment 970105 Expanding Knowledge in the Environmental Sciences Centre for Integrative Ecology Environmental Sustainability Research Group School of Life and Environmental Sciences Text Journal contribution 2014 ftdeakinunifig 2024-06-13T00:21:12Z A method for combining a proportional-hazards survival time model with a bioassay model where the log-hazard function is modelled as a linear or smoothing spline function of log-concentration combined with a smoothing spline function of time is described. The combined model is fitted to mortality numbers, resulting from survival times that are grouped due to a common set of observation times, using Generalized Additive Models (GAMs). The GAM fits mortalities as conditional binomials using an approximation to the log of the integral of the hazard function and is implemented using freely-available, general software for fitting GAMs. Extensions of the GAM are described to allow random effects to be fitted and to allow for time-varying concentrations by replacing time with a calibrated cumulative exposure variable with calibration parameter estimated using profile likelihood. The models are demonstrated using data from a studies of a marine and a, previously published, freshwater taxa. The marine study involved two replicate bioassays of the effect of zinc exposure on survival of an Antarctic amphipod, Orchomenella pinguides. The other example modelled survival of the daphnid, Daphnia magna, exposed to potassium dichromate and was fitted by both the GAM and the process-based DEBtox model. The GAM fitted with a cubic regression spline in time gave a 61 % improvement in fit to the daphnid data compared to DEBtox due to a non-monotonic hazard function. A simulation study using each of these hazard functions as operating models demonstrated that the GAM is overall more accurate in recovering lethal concentration values across the range of forms of the underlying hazard function compared to DEBtox and standard multiple endpoint probit analyses. Article in Journal/Newspaper Antarc* Antarctic DRO - Deakin Research Online Antarctic Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
institution |
Open Polar |
collection |
DRO - Deakin Research Online |
op_collection_id |
ftdeakinunifig |
language |
unknown |
topic |
Dose–response model Generalized Additive Model Grouped survival times Time–response model 050204 Environmental Impact Assessment 970105 Expanding Knowledge in the Environmental Sciences Centre for Integrative Ecology Environmental Sustainability Research Group School of Life and Environmental Sciences |
spellingShingle |
Dose–response model Generalized Additive Model Grouped survival times Time–response model 050204 Environmental Impact Assessment 970105 Expanding Knowledge in the Environmental Sciences Centre for Integrative Ecology Environmental Sustainability Research Group School of Life and Environmental Sciences SG Candy BJ Sfiligoj CK King Julie Mondon Modelling grouped survival times in toxicological studies using generalized additive models |
topic_facet |
Dose–response model Generalized Additive Model Grouped survival times Time–response model 050204 Environmental Impact Assessment 970105 Expanding Knowledge in the Environmental Sciences Centre for Integrative Ecology Environmental Sustainability Research Group School of Life and Environmental Sciences |
description |
A method for combining a proportional-hazards survival time model with a bioassay model where the log-hazard function is modelled as a linear or smoothing spline function of log-concentration combined with a smoothing spline function of time is described. The combined model is fitted to mortality numbers, resulting from survival times that are grouped due to a common set of observation times, using Generalized Additive Models (GAMs). The GAM fits mortalities as conditional binomials using an approximation to the log of the integral of the hazard function and is implemented using freely-available, general software for fitting GAMs. Extensions of the GAM are described to allow random effects to be fitted and to allow for time-varying concentrations by replacing time with a calibrated cumulative exposure variable with calibration parameter estimated using profile likelihood. The models are demonstrated using data from a studies of a marine and a, previously published, freshwater taxa. The marine study involved two replicate bioassays of the effect of zinc exposure on survival of an Antarctic amphipod, Orchomenella pinguides. The other example modelled survival of the daphnid, Daphnia magna, exposed to potassium dichromate and was fitted by both the GAM and the process-based DEBtox model. The GAM fitted with a cubic regression spline in time gave a 61 % improvement in fit to the daphnid data compared to DEBtox due to a non-monotonic hazard function. A simulation study using each of these hazard functions as operating models demonstrated that the GAM is overall more accurate in recovering lethal concentration values across the range of forms of the underlying hazard function compared to DEBtox and standard multiple endpoint probit analyses. |
format |
Article in Journal/Newspaper |
author |
SG Candy BJ Sfiligoj CK King Julie Mondon |
author_facet |
SG Candy BJ Sfiligoj CK King Julie Mondon |
author_sort |
SG Candy |
title |
Modelling grouped survival times in toxicological studies using generalized additive models |
title_short |
Modelling grouped survival times in toxicological studies using generalized additive models |
title_full |
Modelling grouped survival times in toxicological studies using generalized additive models |
title_fullStr |
Modelling grouped survival times in toxicological studies using generalized additive models |
title_full_unstemmed |
Modelling grouped survival times in toxicological studies using generalized additive models |
title_sort |
modelling grouped survival times in toxicological studies using generalized additive models |
publishDate |
2014 |
url |
http://hdl.handle.net/10536/DRO/DU:30069277 https://figshare.com/articles/journal_contribution/Modelling_grouped_survival_times_in_toxicological_studies_using_generalized_additive_models/20917198 |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Antarctic Gam |
geographic_facet |
Antarctic Gam |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_relation |
http://hdl.handle.net/10536/DRO/DU:30069277 https://figshare.com/articles/journal_contribution/Modelling_grouped_survival_times_in_toxicological_studies_using_generalized_additive_models/20917198 |
op_rights |
All Rights Reserved |
_version_ |
1802649636041654272 |