Efficient experimental design for dose response modelling

O’Brien, Timothy E.; Silcox, Jack

doi:10.1080/02664763.2021.1880556

Cited by 4 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the nonlinear regression methods and examples provided here, interested readers may wish to more fully explore topics such as further heteroskedastic (variance function) modelling, bioassay and synergy modelling (Lee et al 2007 ; Lynch et al 2016 ; Sims and O’Brien 2011 ; Straetemans et al 2005 ; Tallarida 2000 ; Wheeler et al 2006 ; White et al 2019 ), multivariate, compartmental, and generalized nonlinear models, related experimental design considerations (Kim et al 2021 ; O’Brien et al 2010 , O’Brien and Silcox 2021 ), and additional curvature examples (Seber and Wild 1989 ). Other notable recent application fields include the use of high-throughput dose response methods to evaluate compounds as potential antiviral drugs to treat COVID-19 patients (Chen et al 2022 ) and modelling to assess enzymatic activity in viral proteins comparing SARS-CoV with SARS-CoV-2 (O’Brien et al 2021 ).…”

Section: Discussion and Final Thoughtsmentioning

confidence: 99%

Nonlinear Regression Modelling: A Primer with Applications and Caveats

O’Brien,

Silcox

2024

Bull Math Biol

Self Cite

View full text Add to dashboard Cite

Use of nonlinear statistical methods and models are ubiquitous in scientific research. However, these methods may not be fully understood, and as demonstrated here, commonly-reported parameter p-values and confidence intervals may be inaccurate. The gentle introduction to nonlinear regression modelling and comprehensive illustrations given here provides applied researchers with the needed overview and tools to appreciate the nuances and breadth of these important methods. Since these methods build upon topics covered in first and second courses in applied statistics and predictive modelling, the target audience includes practitioners and students alike. To guide practitioners, we summarize, illustrate, develop, and extend nonlinear modelling methods, and underscore caveats of Wald statistics using basic illustrations and give key reasons for preferring likelihood methods. Parameter profiling in multiparameter models and exact or near-exact versus approximate likelihood methods are discussed and curvature measures are connected with the failure of the Wald approximations regularly used in statistical software. The discussion in the main paper has been kept at an introductory level and it can be covered on a first reading; additional details given in the Appendices can be worked through upon further study. The associated online Supplementary Information also provides the data and R computer code which can be easily adapted to aid researchers to fit nonlinear models to their data.

show abstract

Section: Discussion and Final Thoughtsmentioning

confidence: 99%

Nonlinear Regression Modelling: A Primer with Applications and Caveats

O’Brien,

Silcox

2024

Bull Math Biol

Self Cite

View full text Add to dashboard Cite

show abstract

“…Logistic regression is well suited to accommodate binomial response data (i.e., dead/alive) from an insecticide bioassay. [38][39][40] In each model, we included insecticide concentration and A. kondoi population as fixed-effect predictors. We also included an observation-level random effect predictorwhereby each data point obtained a unique level of a random effect that models extra variation in the dataoften used to avert model overdispersion.…”

Section: Discussionmentioning

confidence: 99%

“…For each insecticide bioassay, we modelled aphid mortality at each exposure period in a binomial logistic regression. Logistic regression is well suited to accommodate binomial response data (i.e., dead/alive) from an insecticide bioassay 38–40 . In each model, we included insecticide concentration and A. kondoi population as fixed‐effect predictors.…”

Section: Methodsmentioning

confidence: 99%

Discovery of insecticide resistance in field‐collected populations of the aphid pest, Acyrthosiphon kondoi Shinji

Chirgwin,

Thia,

Copping

et al. 2023

Pest Management Science

View full text Add to dashboard Cite

BACKGROUNDThe bluegreen aphid (Acyrthosiphon kondoi) is a worldwide pest of alfalfa, pulses, and other legume crops. A long overreliance on insecticides to control A. kondoi has potentially placed populations under selection pressure favouring resistant phenotypes, but to date, there have been no documented cases of insecticide resistance. Recently, Australian growers began reporting conventional insecticides failing to adequately control A. kondoi populations, prompting this laboratory‐based investigation into whether these populations have evolved resistance.RESULTSWe discovered four A. kondoi populations with moderate resistance (10‐40‐fold) to three different insecticide groups: organophosphates, carbamates and pyrethroids. However, A. kondoi populations showed no resistance to the butenolide, flupyradifurone. We were unable to identify general metabolic mechanisms using synergist assays (P450s, GSTs, or esterases), indicating the need for further detailed molecular investigations to characterise the putative resistance mechanism.CONCLUSIONInsecticide‐resistant A. kondoi present an emerging challenge to Australian agriculture. Growers require new tools and updated strategies, including access to newer chemistries, to alleviate their reliance on the few insecticides currently registered against A. kondoi. The implications of insecticide resistant A. kondoi for future management, the potential mechanisms of resistance, and future research priorities are discussed.This article is protected by copyright. All rights reserved.

show abstract

Dose–response curves: the next frontier in plant ecology

Veresoglou,

Begum

2024

Trends in Plant Science

View full text Add to dashboard Cite

Efficient experimental design for dose response modelling

Cited by 4 publications

References 14 publications

Nonlinear Regression Modelling: A Primer with Applications and Caveats

Nonlinear Regression Modelling: A Primer with Applications and Caveats

Discovery of insecticide resistance in field‐collected populations of the aphid pest, Acyrthosiphon kondoi Shinji

Dose–response curves: the next frontier in plant ecology

Contact Info

Product

Resources

About