The Experts below are selected from a list of 16803 Experts worldwide ranked by ideXlab platform
Bernard Rachet - One of the best experts on this subject based on the ideXlab platform.
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adjusting for Overdispersion in piecewise exponential regression models to estimate excess mortality rate in population based research
BMC Medical Research Methodology, 2016Co-Authors: Miguel Angel Luquefernandez, Aurelien Belot, Manuela Quaresma, Camille Maringe, Michel P Coleman, Bernard RachetAbstract:In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for Overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for Overdispersion. We used a regression-based score test for Overdispersion under the relative survival framework and proposed different approaches to correct for Overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent Overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest Overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, Overdispersion due to model misspecification and true or inherent Overdispersion.
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Adjusting for Overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.
BMC medical research methodology, 2016Co-Authors: Miguel Angel Luque-fernandez, Aurelien Belot, Manuela Quaresma, Camille Maringe, Michel P Coleman, Bernard RachetAbstract:In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for Overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for Overdispersion. We used a regression-based score test for Overdispersion under the relative survival framework and proposed different approaches to correct for Overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent Overdispersion (p-value
André A. Fenton - One of the best experts on this subject based on the ideXlab platform.
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attention like modulation of hippocampus place cell discharge
The Journal of Neuroscience, 2010Co-Authors: André A. Fenton, Larissa Zinyuk, William W Lytton, Jeremy M Barry, Pierrepascal Lencksantini, Stepan Kubik, J Bures, Bruno PoucetAbstract:Hippocampus place cell discharge is an important model system for understanding cognition, but evidence is missing that the place code is under the kind of dynamic attentional control characterized in primates as selective activation of one neural representation and suppression of another, competing representation. We investigated the apparent noise (“Overdispersion”) in the CA1 place code, hypothesizing that Overdispersion results from discharge fluctuations as spatial attention alternates between distal cues and local/self-motion cues. The hypothesis predicts that: (1) preferential use of distal cues will decrease Overdispersion; (2) global, attention-like states can be decoded from ensemble discharge such that both the discharge rates and the spatial firing patterns of individual cells will be distinct in the two states; (3) identifying attention-like states improves reconstructions of the rat9s path from ensemble discharge. These predictions were confirmed, implying that a covert, dynamic attention-like process modulates discharge on a ∼1 s time scale. We conclude the hippocampus place code is a dynamic representation of the spatial information in the immediate focus of attention.
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properties of the extra positional signal in hippocampal place cell discharge derived from the Overdispersion in location specific firing
Neuroscience, 2002Co-Authors: André A. Fenton, Andrey V. Olypher, Petr LanskýAbstract:Abstract There is a good deal of evidence that in the rodent, an internal model of the external world is encoded by hippocampal pyramidal cells, called ‘place cells’. During free exploration, the activity of place cells is higher within a small part of the space, called the firing field, and virtually silent elsewhere. We have previously shown that the spiking activity during passes through the firing field is characterized not only by the high firing rate, but also by its very high variability (‘Overdispersion’). This Overdispersion indicates that place cells carry information in addition to position. Here we demonstrate by simulations of an integrate-and-fire neuronal model that while a rat is foraging in an open space this additional information may arise from a process that alternatingly modulates the inputs to place cells by about 10% with a mean period of about 1 s. We propose that the Overdispersion reflects switches of the rats attention between different spatial reference frames of the environment. This predicts that the Overdispersion will not be observed in rats that use only room-based cues for navigation. We show that while place cell firing is overdispersed in rats during foraging in an open arena, the firing is less overdispersed during the same behavior in the same environment, when the rats have been trained to use only room-based and not arena-based cues to navigate.
Xavier A Harrison - One of the best experts on this subject based on the ideXlab platform.
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A comparison of observation-level random effect and Beta-Binomial models for modelling Overdispersion in Binomial data in ecology & evolution
PeerJ, 2015Co-Authors: Xavier A HarrisonAbstract:Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the Overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling Overdispersion when it is present is therefore critical for robust biological inference. One means to account for Overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model Overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of Overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (
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a comparison of observation level random effect and beta binomial models for modelling Overdispersion in binomial data in ecology evolution
PeerJ, 2015Co-Authors: Xavier A HarrisonAbstract:Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the Overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling Overdispersion when it is present is therefore critical for robust biological inference. One means to account for Overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model Overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of Overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels), I investigate the performance of both model types under a range of random effect sample sizes when Overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the Overdispersion; OLRE failed to cope with Overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for Overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed poorly when models contained <5 levels of the random intercept term, especially for estimating variance components, and this effect appeared independent of total sample size. These results suggest that OLRE are a useful tool for modelling Overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.
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using observation level random effects to model Overdispersion in count data in ecology and evolution
PeerJ, 2014Co-Authors: Xavier A HarrisonAbstract:Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated) data, or an excess frequency of zeroes (zero-inflation). Accounting for Overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE), where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with Overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with Overdispersion are scarce. Here I use simulations to show that in cases where Overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when Overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of Overdispersion. There was a positive relationship between the magnitude of Overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for Overdispersion in mixed models can erroneously inflate measures of explained variance (r2), which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for Overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of Overdispersion and must be applied judiciously.
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using observation level random effects to model Overdispersion in count data in ecology and evolution
PeerJ, 2014Co-Authors: Xavier A HarrisonAbstract:Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated) data, or an excess frequency of zeroes (zero-inflation). Accounting for Overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE), where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with Overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with Overdispersion are scarce. Here I use simulations to show that in cases where Overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when Overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of Overdispersion. There was a positive relationship between the magnitude of Overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for Overdispersion in mixed models can erroneously inflate measures of explained variance (r2), which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for Overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of Overdispersion and must be applied judiciously.
Camille Maringe - One of the best experts on this subject based on the ideXlab platform.
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adjusting for Overdispersion in piecewise exponential regression models to estimate excess mortality rate in population based research
BMC Medical Research Methodology, 2016Co-Authors: Miguel Angel Luquefernandez, Aurelien Belot, Manuela Quaresma, Camille Maringe, Michel P Coleman, Bernard RachetAbstract:In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for Overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for Overdispersion. We used a regression-based score test for Overdispersion under the relative survival framework and proposed different approaches to correct for Overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent Overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest Overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, Overdispersion due to model misspecification and true or inherent Overdispersion.
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Adjusting for Overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.
BMC medical research methodology, 2016Co-Authors: Miguel Angel Luque-fernandez, Aurelien Belot, Manuela Quaresma, Camille Maringe, Michel P Coleman, Bernard RachetAbstract:In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for Overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for Overdispersion. We used a regression-based score test for Overdispersion under the relative survival framework and proposed different approaches to correct for Overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent Overdispersion (p-value
Manuela Quaresma - One of the best experts on this subject based on the ideXlab platform.
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adjusting for Overdispersion in piecewise exponential regression models to estimate excess mortality rate in population based research
BMC Medical Research Methodology, 2016Co-Authors: Miguel Angel Luquefernandez, Aurelien Belot, Manuela Quaresma, Camille Maringe, Michel P Coleman, Bernard RachetAbstract:In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for Overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for Overdispersion. We used a regression-based score test for Overdispersion under the relative survival framework and proposed different approaches to correct for Overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent Overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest Overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, Overdispersion due to model misspecification and true or inherent Overdispersion.
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Adjusting for Overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.
BMC medical research methodology, 2016Co-Authors: Miguel Angel Luque-fernandez, Aurelien Belot, Manuela Quaresma, Camille Maringe, Michel P Coleman, Bernard RachetAbstract:In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for Overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for Overdispersion. We used a regression-based score test for Overdispersion under the relative survival framework and proposed different approaches to correct for Overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent Overdispersion (p-value
