scholarly journals Predicting individual shelter dog behaviour after adoption using longitudinal behavioural assessment: a hierarchical Bayesian approach

2021 ◽  
Author(s):  
Conor Goold ◽  
Ruth C. Newberry
Keyword(s):  
Measurement Error ◽  
Predictive Ability ◽  
Accurate Estimation ◽  
Hierarchical Bayesian ◽  
Longitudinal Assessment ◽  
Telephone Surveys ◽  
Individual Level ◽  
Behavioural Assessment ◽  
Bayesian Mixture ◽  
Highest Density Interval

Predicting the behaviour of shelter dogs after adoption is an important, but difficult, endeavour. Differences between shelter and post-adoption environments, between- and within-individual heterogeneity in behaviour, uncertainty in behavioural predictions and measurement error all hinder the accurate assessment of future behaviour. This study integrates 1) a longitudinal behavioural assessment with 2) a novel joint hierarchical Bayesian mixture model that accounts for individual variation, missing data and measurement error to predict behaviour post-adoption. We analysed shelter observations (> 28,000 records) and post-adoption reports (from telephone surveys) on the behaviour of 241 dogs across eight contexts. Dog behaviour at the shelter correlated positively with behaviour post-adoption within contexts (r = 0.38; 95% highest density interval: [0.20, 0.55]), and behavioural repeatability was approximately 20% higher post-adoption for behaviour within contexts. Although measurement error was higher post-adoption than at the shelter, we found few differences in individual-level, latent probabilities of different behaviours post-adoption versus at the shelter. This good predictive ability was aided by accurate representation of uncertainty in individual-level predictions. We conclude that longitudinal assessment paired with a sufficient inferential framework to model latent behavioural profiles with uncertainty enables reasonably accurate estimation of post-adoption behaviour.

Individual Differences in the Neural Dynamics of Response Inhibition

2019 ◽  
Vol 31 (12) ◽  
pp. 1976-1996 ◽  
Author(s):  
M. Fiona Molloy ◽  
Giwon Bahg ◽  
Zhong-Lin Lu ◽  
Brandon M. Turner
Keyword(s):  
Individual Differences ◽  
Cognitive Control ◽  
Response Inhibition ◽  
Bayesian Models ◽  
Accurate Estimation ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Data Set ◽  
Individual Level ◽  
Aggregate Information

Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.

Zooplankton and the total phosphorus – chlorophyll a relationship: hierarchical Bayesian analysis of measurement error

2008 ◽  
Vol 65 (12) ◽  
pp. 2644-2655 ◽  
Author(s):  
Amy M. Kamarainen ◽  
Freya E. Rowland ◽  
Reinette Biggs ◽  
Stephen R. Carpenter
Keyword(s):  
Measurement Error ◽  
Chlorophyll A ◽  
Total Phosphorus ◽  
Empirical Studies ◽  
Predictive Ability ◽  
Zooplankton Biomass ◽  
Hierarchical Bayesian ◽  
Total Zooplankton ◽  
Residual Variation ◽  
Total Zooplankton Biomass

Zooplankton grazing is important in resolving residual variation around the total phosphorus – chlorophyll a relationship. In empirical studies, zooplankton body size is often a better predictor of residual variation than zooplankton biomass. We investigate whether higher measurement error associated with zooplankton biomass may explain its lower predictive ability. We collected five replicate zooplankton biomass samples in 19 lakes, allowing us to quantify measurement error in volumetric zooplankton biomass with greater precision than in previous studies. A hierarchical Bayesian model was used to assess the predictive ability of volumetric zooplankton biomass and mean individual zooplankton length, corrected for measurement error. We found consistent effects of total zooplankton biomass, but not zooplankton length, on chlorophyll a. This finding does not appear to be related to the higher precision with which total zooplankton biomass was measured in our study, but rather to ecological factors. Interlake variation outweighed the effects of measurement error in estimating the strength of relationships between zooplankton variables and chlorophyll a. Our findings therefore suggest that studies to estimate zooplankton effects on phytoplankton should allocate resources to study a larger range of lakes over different time periods than to process replicate samples to reduce measurement error.

The Bias of Individuals (In Crowds): Why Implicit Bias is Probably a Noisily-Measured Individual-Level Construct

2020 ◽  
Author(s):  
Paul Robert Connor ◽  
Ellen Riemke Katrien Evers
Keyword(s):  
Measurement Error ◽  
Present Article ◽  
Implicit Bias ◽  
Empirical Support ◽  
Simulated Data ◽  
Individual Level ◽  
Substantial Error

Payne, Vuletich, and Lundberg’s bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues’ proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors’ counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted “implicit-bias-based expulsion program” within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/.

scholarly journals How well do neurosurgeons predict survival in patients with high-grade glioma?

2021 ◽  
Author(s):  
Lisa Millgård Sagberg ◽  
Asgeir S. Jakola ◽  
Ingerid Reinertsen ◽  
Ole Solheim
Keyword(s):  
Median Survival ◽  
Survival Curve ◽  
Binary Logistic Regression ◽  
High Grade Glioma ◽  
Accurate Estimation ◽  
Binary Logistic Regression Analysis ◽  
High Grade ◽  
Clinical Prediction ◽  
Individual Level ◽  
Actual Survival

AbstractDue to the lack of reliable prognostic tools, prognostication and surgical decisions largely rely on the neurosurgeons’ clinical prediction skills. The aim of this study was to assess the accuracy of neurosurgeons’ prediction of survival in patients with high-grade glioma and explore factors possibly associated with accurate predictions. In a prospective single-center study, 199 patients who underwent surgery for high-grade glioma were included. After surgery, the operating surgeon predicted the patient’s survival using an ordinal prediction scale. A survival curve was used to visualize actual survival in groups based on this scale, and the accuracy of clinical prediction was assessed by comparing predicted and actual survival. To investigate factors possibly associated with accurate estimation, a binary logistic regression analysis was performed. The surgeons were able to differentiate between patients with different lengths of survival, and median survival fell within the predicted range in all groups with predicted survival < 24 months. In the group with predicted survival > 24 months, median survival was shorter than predicted. The overall accuracy of surgeons’ survival estimates was 41%, and over- and underestimations were done in 34% and 26%, respectively. Consultants were 3.4 times more likely to accurately predict survival compared to residents (p = 0.006). Our findings demonstrate that although especially experienced neurosurgeons have rather good predictive abilities when estimating survival in patients with high-grade glioma on the group level, they often miss on the individual level. Future prognostic tools should aim to beat the presented clinical prediction skills.

scholarly journals Mendelian randomisation for mediation analysis: current methods and challenges for implementation

2021 ◽  
Author(s):  
Alice R. Carter ◽  
Eleanor Sanderson ◽  
Gemma Hammerton ◽  
Rebecca C. Richmond ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Instrumental Variable ◽  
Real Data ◽  
Mendelian Randomisation ◽  
Differential Measurement ◽  
Individual Level ◽  
Differential Measurement Error ◽  
Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

scholarly journals Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

2021 ◽  
Vol 13 (8) ◽  
pp. 1519
Author(s):  
Kensuke Kawamura ◽  
Tomohiro Nishigaki ◽  
Andry Andriamananjara ◽  
Hobimiarantsoa Rakotonindrina ◽  
Yasuhiro Tsujimoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Soil Phosphorus ◽  
Predictive Ability ◽  
Available P ◽  
Accurate Estimation ◽  
One Dimensional ◽  
P Content ◽  
Soil Available P

As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.

Predictive ability of heel quantitative ultrasound for incident fractures: an individual-level meta-analysis

2015 ◽  
Vol 26 (7) ◽  
pp. 1979-1987 ◽  
Author(s):  
E. V. McCloskey ◽  
J. A. Kanis ◽  
A. Odén ◽  
N. C. Harvey ◽  
D. Bauer ◽  
...  
Keyword(s):  
Quantitative Ultrasound ◽  
Meta Analysis ◽  
Predictive Ability ◽  
Individual Level

scholarly journals Mendelian randomisation for mediation analysis: current methods and challenges for implementation

2019 ◽  
Author(s):  
Alice R Carter ◽  
Eleanor Sanderson ◽  
Gemma Hammerton ◽  
Rebecca C Richmond ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Real Data ◽  
Mendelian Randomisation ◽  
Reverse Causality ◽  
Differential Measurement ◽  
Individual Level ◽  
Differential Measurement Error ◽  
Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Mediation analysis experiences a number of methodological difficulties, including bias due to confounding and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable Mendelian randomisation (MVMR) and two-step Mendelian randomisation. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although Mendelian randomisation relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our examples demonstrate that it is unlikely to be affected by confounders of the exposure or mediator and the outcome, reverse causality and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR, and can improve causal inference in mediation analysis.

Probing the Links Between Team Resilience, Competitive Advantage, and Organizational Effectiveness: Evidence from Information Technology Industry

2020 ◽  
Vol 8 (2) ◽  
pp. 289-307
Author(s):  
Shikha Sharma ◽  
Sanjeev K. Sharma
Keyword(s):  
Competitive Advantage ◽  
Organizational Effectiveness ◽  
Predictive Ability ◽  
Business Environment ◽  
Information Technology Industry ◽  
Design Data ◽  
Individual Level ◽  
Northern India ◽  
Team Resilience ◽  
The Relationship

Modern business environment throws hard punches not just at an individual level but at team level too. In today’s sweeping and highly complex environment, top teams need to be more than just high performers. They need to adapt and thrive, regardless of the turbulence they face. Research revealed that resilient teams can be more agile and adjust easily with the varying demands of its market. Appreciating the importance of resilience in team-context, this study examined the relationship among team resilience (TR), competitive advantage (CA), and organizational effectiveness (OE). Using descriptive research design data were collected from 300 employees aggregated into 71 teams from 18 IT firms located in Northern India. Statistical analyses found a significant relationship between TR and OE. CA was found to be a partial mediator among the relationship between TR and OE. Findings underline the strength of a relationship and predictive ability of various dimensions of TR with OE and CA. Based on the empirical results, the researcher proposed a Team Resilience Building Framework (TRBF). The organizations may benefit from the research findings in terms of devising the strategies to enhance resilience capacity of their teams.

The Bias of Individuals (in Crowds): Why Implicit Bias Is Probably a Noisily Measured Individual-Level Construct

2020 ◽  
Vol 15 (6) ◽  
pp. 1329-1345 ◽  
Author(s):  
Paul Connor ◽  
Ellen R. K. Evers
Keyword(s):  
Measurement Error ◽  
Present Article ◽  
Implicit Bias ◽  
Empirical Support ◽  
Simulated Data ◽  
Individual Level ◽  
Substantial Error

Payne, Vuletich, and Lundberg’s bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues’ proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors’ counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted “implicit-bias-based expulsion program” within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/ .

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