All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019)

Wilson, Michael David; Boag, Russell J.; Strickland, Luke

doi:10.1007/s42113-019-00054-x

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…7) involved in calculating the model. Compliance with these conditions helps not only for the "usefulness" of models for logBBB but also their reproducibility [27].…”

Section: Last But Not Leastmentioning

confidence: 99%

The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds

Toropova

Toropov

Roncaglioni

et al. 2023

Preprint

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In order to apply quantitative relationships "structure-endpoint" approach its reliability of prediction is necessary but sometimes challenging to achieve. Here, an attempt is made to accomplish the reliability of forecasts by creating a set of random partitions of data into training and validation sets, followed by constructing random models. A system of random models for a useful approach should be self-consistent, giving a similar or at least comparable statistical quality of the predictions for models obtained using different splits of available data into training and validation sets. Developed computer experiments aimed at obtaining blood-brain barrier permeation models showed that, in principle, such an approach can be used for the above purpose taking advantage of specific algorithms to optimize the modelling steps. Results so obtained are good, and better than what reported previously. The suggested approach to validation of models is non-identic to traditionally applied manners of the checking up models. The concept of validation can be used for arbitrary models (not only for models of the blood-brain barrier).

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“…7) involved in calculating the model. Compliance with these conditions helps not only for the "usefulness" of models for logBBB but also their reproducibility [27].…”

Section: Last But Not Leastmentioning

confidence: 99%

The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds

Toropova

Toropov

Roncaglioni

et al. 2023

Preprint

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“…There are strong arguments for going beyond this minimum state of reproducibility of providing only formulae, towards a gold standard in which flexible model implementations are provided with journal articles (G. C. Banks et al, 2018;R. D. Peng, 2011;Wilson et al, 2019).…”

Section: Improve Open Science Practicesmentioning

confidence: 99%

The next generation of fatigue prediction models: evaluating current trends in biomathematical modelling

Wilson

Strickland

Ballard

et al. 2022

Theoretical Issues in Ergonomics Science

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“…Firstly, BMM researchers have typically only provided the mathematical derivations of their models (i.e., formulae) and not their computational implementations. This is a barrier to reproducibility (Wilson, Boag, & Strickland, 2019) because implementing BMMs and the required data structures from the ground up requires substantial expertise and time investment. Prior to FIPS, the only available BMM implementations were contained within closed-source commercial software (e.g., SAFTE-FAST; Hursh et al, 2004).…”

Section: Background On Biomathematical Modelsmentioning

confidence: 99%

FIPS: An R Package for Biomathematical Modelling of Human Fatigue Related Impairment

Wilson¹,

Strickland²,

Ballard³

2020

JOSS

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In many workplace contexts, accurate predictions of a human's fatigue state can drastically improve system safety. Biomathematical models of fatigue (BMMs) are a family of dynamic phenomenological models that predict the neurobehavioural outcomes of fatigue (e.g., sleepiness, performance impairment) based on sleep/wake history (Dawson, Darwent, & Roach, 2017). However, to-date there are no open source implementations of BMMs, and this presents a significant barrier to their broadscale adoption by researchers and industry practitioners. FIPS is an open source R package (R Core Team, 2020) to facilitate BMM research and simulation. FIPS has implementations of several published bio-mathematical models and includes functions for easily manipulating sleep history data into the required data structures. FIPS also includes default plot and summary methods to aid model interpretation. Model objects follow tidy data conventions (Wickham, 2014), enabling FIPS to be integrated into existing research workflows of R users.

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All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019)

Cited by 11 publications

References 7 publications

The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds

The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds

The next generation of fatigue prediction models: evaluating current trends in biomathematical modelling

FIPS: An R Package for Biomathematical Modelling of Human Fatigue Related Impairment

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