1,720,957 research outputs found
Bayesian Nonlinear Hierarchical Models: Applications in Preclinical Pharmacometrics
No full textAlthough Bayesian methods are expanding considerably in various scientific areas, their applications in the field of pharmacokinetic/pharmacodynamic (PK/PD) modelling and simulation is still relatively limited. In this work, Bayesian techniques are used to facilitate the estimation of a novel PK/PD model which is developed to quantify the extent of PD synergy between two compounds using historical in vivo data. The model is fitted using package rstan, the R interface to Stan. Stan is a recently developed software package which allows an efficient estimation using the No-U-Turn Sampler (NUTS).
Since the data consist of a series of 11 trials performed sequentially, a Bayesian sequential integration is considered: the posteriors resulting from the analysis of one trial are used to specify the hyperparameters of the priors of the next trial. The recursive update of posterior distributions whenever new information is available is less computationally intensive compared to the analysis of all data up to the current trial. However, this method implies the analysis of a limited amount of information during the first integration steps, which may hinder the estimation process. The aim of the present work is to discuss challenges as well as opportunities which are related to the impact of (i) prior specification, (ii) random effect choice and (iii) experimental design. In addition, the results from an extensive simulation study assessing the performance of the Bayesian sequential integration for an increasing model complexity are evaluated.
The results suggest that the use of informative prior distributions reduces the correlation among parameters and improves the accuracy of estimates. Moreover, choosing the random effect on a parameter that is not highly correlated with others avoids overcompensations, thus ensuring better predictions. On top of that, trials should be designed so that each of them explores an exhaustive number of doses and sampling times.European Union's Horizon 2020 research and innovation programme, Grant/Award Number: 633567; IAP Research Network P7/06 of the Belgian State (Belgian Science Policy); French National Cancer Institute (INCa), Grant/Award Number: INCA 953
Bayesian Nonlinear Hierarchical Models: Applications in Preclinical Pharmacometrics
No full textAlthough Bayesian methods are expanding considerably in various scientific areas, their applications in the field of pharmacokinetic/pharmacodynamic (PK/PD) modelling and simulation is still relatively limited. In this work, Bayesian techniques are used to facilitate the estimation of a novel PK/PD model which is developed to quantify the extent of PD synergy between two compounds using historical in vivo data. The model is fitted using package rstan, the R interface to Stan. Stan is a recently developed software package which allows an efficient estimation using the No-U-Turn Sampler (NUTS).
Since the data consist of a series of 11 trials performed sequentially, a Bayesian sequential integration is considered: the posteriors resulting from the analysis of one trial are used to specify the hyperparameters of the priors of the next trial. The recursive update of posterior distributions whenever new information is available is less computationally intensive compared to the analysis of all data up to the current trial. However, this method implies the analysis of a limited amount of information during the first integration steps, which may hinder the estimation process. The aim of the present work is to discuss challenges as well as opportunities which are related to the impact of (i) prior specification, (ii) random effect choice and (iii) experimental design. In addition, the results from an extensive simulation study assessing the performance of the Bayesian sequential integration for an increasing model complexity are evaluated.
The results suggest that the use of informative prior distributions reduces the correlation among parameters and improves the accuracy of estimates. Moreover, choosing the random effect on a parameter that is not highly correlated with others avoids overcompensations, thus ensuring better predictions. On top of that, trials should be designed so that each of them explores an exhaustive number of doses and sampling times.European Union's Horizon 2020 research and innovation programme, Grant/Award Number: 633567; IAP Research Network P7/06 of the Belgian State (Belgian Science Policy); French National Cancer Institute (INCa), Grant/Award Number: INCA 953
Bayesian pooling versus sequential integration of small preclinical trials: a comparison within linear and nonlinear modeling frameworks
No full textBayesian sequential integration is an appealing approach in drug development, as it allows to recursively update posterior distributions as soon as new data become available, thus considerably reducing the computation time. However, preclinical trials are often characterized by small sample sizes, which may affect the estimation process during the first integration steps, particularly when complex PK-PD models are used. In this case, sequential integration would not be practicable, and trials should be pooled together. This work is aimed at comparing simple Bayesian pooling with sequential integration through a simulation study. The two techniques are compared under several scenarios using linear as well as nonlinear models. The results of our simulation study encourage the use of Bayesian sequential integration with linear models. However, in the case of nonlinear models several caveats arise. This paper outlines some important recommendations and precautions in that respect.La Gamba, F (corresponding author), Janssen Res & Dev, Dept Quantitat Sci, Beerse, Belgium; Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium.
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A Bayesian K‐PD model for synergy: A case study
No full textCoadministration of 2 or more compounds can alter both the pharmacokinetics and pharmacodynamics of individual compounds. While experiments on pharmacodynamic drug-drug interactions are usually performed in an in vitro setting, this experiment focuses on an in vivo setting. The change over time of a safety biomarker is modeled using an indirect response model, in which the virtual pharmacokinetic profile of one compound drives the effect of the other. Several experiments at different dose level combinations were performed sequentially. While a traditional frequentist analysis consists of estimating the model parameters based on all the data simultaneously, in this work, we consider a Bayesian inference framework allowing to incorporate the results from a historical dose-response experiment
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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