Relevant bibliographies by topics / Bayesian statistical analysi

Academic literature on the topic 'Bayesian statistical analysi'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Bayesian statistical analysi"

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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN." PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, no. 2 (June 1, 2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.

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In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of global (Bayesian). Survival Bayesial parameter estimation using MCMC algorithms for model complex / complicated and difficult to resolve while the Cox regression models using the method of partial likelihood. Results of the parameter estimates do not close form that needs to be done by the method of Newton-Raphson iteration.
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Davidson, Russell. "An Agnostic Look at Bayesian Statistics and Econometrics." Review of Economic Analysis 2, no. 2 (August 6, 2010): 153–68. http://dx.doi.org/10.15353/rea.v2i2.1470.

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Bayesians and non-Bayesians, often called frequentists, seem to be perpetually at loggerheads on fundamental questions of statistical inference. This paper takes as agnostic a stand as is possible for a practising frequentist, and tries to elicit a Bayesian answer to questions of interest to frequentists. The argument is based on my presentation at a debate organised by the Rimini Centre for Economic Analysis, between me as the frequentist “advocate”, and Christian Robert on the Bayesian side.
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Ickstadt, Katja, Martin Schäfer, and Manuela Zucknick. "Toward Integrative Bayesian Analysis in Molecular Biology." Annual Review of Statistics and Its Application 5, no. 1 (March 7, 2018): 141–67. http://dx.doi.org/10.1146/annurev-statistics-031017-100438.

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Gelfand, Alan E., and Sudipto Banerjee. "Bayesian Modeling and Analysis of Geostatistical Data." Annual Review of Statistics and Its Application 4, no. 1 (March 7, 2017): 245–66. http://dx.doi.org/10.1146/annurev-statistics-060116-054155.

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Hicks, Tyler, Liliana Rodríguez-Campos, and Jeong Hoon Choi. "Bayesian Posterior Odds Ratios." American Journal of Evaluation 39, no. 2 (May 23, 2017): 278–89. http://dx.doi.org/10.1177/1098214017704302.

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To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.
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Shin, Jennifer J., and David Zurakowski. "Null Hypotheses, Interval Estimation, and Bayesian Analysis." Otolaryngology–Head and Neck Surgery 157, no. 6 (December 2017): 919–20. http://dx.doi.org/10.1177/0194599817728898.

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Hypothesis testing using a frequentist approach is the mainstay of biostatistics and forms the foundation for assessing the significance of study results. This classical method has well-understood advantages as it determines whether data are statistically improbable and provides a threshold (ie, the P value) for delineating significance. Alternative statistical approaches have been proposed, including Bayesian analysis. This technique incorporates a prior probability as to what is already known clinically with the observed data. It is important for otolaryngologists to understand the advantages and disadvantages of these 2 approaches to conduct the most appropriate analyses.
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Donnison, J. R., and M. P. Wiper. "Bayesian statistical analysis of asteroid rotation rates." Monthly Notices of the Royal Astronomical Society 302, no. 1 (January 1, 1999): 75–80. http://dx.doi.org/10.1046/j.1365-8711.1999.02075.x.

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Brophy, James M., and Lawrence Joseph. "Bayesian interim statistical analysis of randomised trials." Lancet 349, no. 9059 (April 1997): 1166–68. http://dx.doi.org/10.1016/s0140-6736(96)06377-5.

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CLINTON, JOSHUA, SIMON JACKMAN, and DOUGLAS RIVERS. "The Statistical Analysis of Roll Call Data." American Political Science Review 98, no. 2 (May 2004): 355–70. http://dx.doi.org/10.1017/s0003055404001194.

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We develop a Bayesian procedure for estimation and inference for spatial models of roll call voting. This approach is extremely flexible, applicable to any legislative setting, irrespective of size, the extremism of the legislators' voting histories, or the number of roll calls available for analysis. The model is easily extended to let other sources of information inform the analysis of roll call data, such as the number and nature of the underlying dimensions, the presence of party whipping, the determinants of legislator preferences, and the evolution of the legislative agenda; this is especially helpful since generally it is inappropriate to use estimates of extant methods (usually generated under assumptions of sincere voting) to test models embodying alternate assumptions (e.g., log-rolling, party discipline). A Bayesian approach also provides a coherent framework for estimation and inference with roll call data that eludes extant methods; moreover, via Bayesian simulation methods, it is straightforward to generate uncertainty assessments or hypothesis tests concerning any auxiliary quantity of interest or to formally compare models. In a series of examples we show how our method is easily extended to accommodate theoretically interesting models of legislative behavior. Our goal is to provide a statistical framework for combining the measurement of legislative preferences with tests of models of legislative behavior.
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Fang, Kai-Tai, and Runze Li. "Bayesian Statistical Inference on Elliptical Matrix Distributions." Journal of Multivariate Analysis 70, no. 1 (July 1999): 66–85. http://dx.doi.org/10.1006/jmva.1998.1816.

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Dissertations / Theses on the topic "Bayesian statistical analysi"

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Ma, Yimin. "Bayesian and empirical Bayesian analysis for the truncation parameter distribution families /." *McMaster only, 1998.

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CHIESA, DAVIDE. "Development and experimental validation of a Monte Carlo simulation model for the Triga Mark II reactor." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/50064.

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In recent years, many computer codes, based on Monte Carlo methods or deterministic calculations, have been developed to separately analyze different aspects regarding nuclear reactors. Nuclear reactors are very complex systems, which require an integrated analysis of all the variables which are intrinsically correlated: neutron fluxes, reaction rates, neutron moderation and absorption, thermal and power distributions, heat generation and transfer, criticality coefficients, fuel burnup, etc. For this reason, one of the main challenges in the analysis of nuclear reactors is the coupling of neutronics and thermal-hydraulics simulation codes, with the purpose of achieving a good modeling and comprehension of the mechanisms which rule the transient phases and the dynamic behavior of the reactor. This is very important to guarantee the control of the chain reaction, for a safe operation of the reactor. In developing simulation tools, benchmark analyses are needed to prove the reliability of the simulations. The experimental measurements conceived to be compared with the results coming out from the simulations are really precious and can provide useful information to improve the description of the physics phenomena in the simulation models. My PhD research activity was held in this framework, as part of the research project Analysis of Reactor COre (ARCO, promoted by INFN) whose task was the development of modern, flexible and integrated tools for the analysis of nuclear reactors, relying on the experimental data collected at the research reactor TRIGA Mark II, installed at the Applied Nuclear Energy Laboratory (LENA) at the University of Pavia. In this way, once the effectiveness and the reliability of these tools for modeling an experimental reactor have been demonstrated, these could be applied to develop new generation systems. In this thesis, I present the complete neutronic characterization of the TRIGA Mark II reactor, which was analyzed in different operating conditions through experimental measurements and the development of a Monte Carlo simulation tool (relied on the MCNP code) able to take into account the ever increasing complexity of the conditions to be simulated. First of all, after giving an overview of some theoretical concepts which are fundamental for the nuclear reactor analysis, a model that reconstructs the first working period of the TRIGA Mark II reactor, in which the “fresh” fuel was not heavily contaminated with fission reaction products, is described. In particular, all the geometries and the materials are described in the MCNP simulation model with good detail, in order to reconstruct the reactor criticality and all the effects on the neutron distributions. The very good results obtained from the simulations of the reactor at low power condition -in which the fuel elements can be considered to be in thermal equilibrium with the water around them- are then used to implement a model for simulating the full power condition (250kW), in which the effects arising from the temperature increase in the fuel-moderator must be taken into account. The MCNP simulation model was exploited to evaluate the reactor power distribution and a dedicated experimental campaign was performed to measure the water temperature within the reactor core. In this way, through a thermal-hydraulic calculation tool, it has been possible to determine the temperature distribution within the fuel elements and to include the description of the thermal effects in the MCNP simulation model. Thereafter, since the neutron flux is a crucial parameter affecting the reaction rates and thus the fuel burnup, its energy and space distributions are analyzed presenting the results of several neutron activation measurements. Particularly, the neutron flux was firstly measured in the reactor's irradiation facilities through the neutron activation of many different isotopes. Hence, in order to analyze the energy flux spectra, I implemented an analysis tool, based on Bayesian statistics, which allows to combine the experimental data from the different activated isotopes and reconstruct a multi-group flux spectrum. Subsequently, the spatial neutron flux distribution within the core was measured by activating several aluminum-cobalt samples in different core positions, thus allowing the determination of the integral and fast flux distributions from the analysis of cobalt and aluminum, respectively. Finally, I present the results of the fuel burnup calculations, that were performed for simulating the current core configuration after a 48 years-long operation. The good accuracy that was reached in the simulation of the neutron fluxes, as confirmed by the experimental measurements, has allowed to evaluate the burnup of each fuel element from the knowledge of the operating hours and the different positions occupied in the core over the years. In this way, it has been possible to exploit the MCNP simulation model to determine a new optimized core configuration which could ensure, at the same time, a higher reactivity and the use of less fuel elements. This configuration was realized in September 2013 and the experimental results confirm the high quality of the work done. The results of this Ph.D. thesis highlight that it is possible to implement analysis tools -ranging from Monte Carlo simulations to the fuel burnup time evolution software, from neutron activation measurements to the Bayesian statistical analysis of flux spectra, and from temperature measurements to thermal-hydraulic models-, which can be appropriately exploited to describe and comprehend the complex mechanisms ruling the operation of a nuclear reactor. Particularly, it was demonstrated the effectiveness and the reliability of these tools in the case of an experimental reactor, where it was possible to collect many precious data to perform benchmark analyses. Therefore, for as these tools have been developed and implemented, they can be used to analyze other reactors and, possibly, to project and develop new generation systems, which will allow to decrease the production of high-level nuclear waste and to exploit the nuclear fuel with improved efficiency.
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Fung, Wing-kam Tony. "Analysis of outliers using graphical and quasi-Bayesian methods /." [Hong Kong] : University of Hong Kong, 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1236146X.

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Woodard, Roger. "Bayesian hierarchical models for hunting success rates /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9951135.

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Oleson, Jacob J. "Bayesian spatial models for small area estimation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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馮榮錦 and Wing-kam Tony Fung. "Analysis of outliers using graphical and quasi-Bayesian methods." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31230842.

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Brody-Moore, Peter. "Bayesian Hierarchical Meta-Analysis of Asymptomatic Ebola Seroprevalence." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2228.

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The continued study of asymptomatic Ebolavirus infection is necessary to develop a more complete understanding of Ebola transmission dynamics. This paper conducts a meta-analysis of eight studies that measure seroprevalence (the number of subjects that test positive for anti-Ebolavirus antibodies in their blood) in subjects with household exposure or known case-contact with Ebola, but that have shown no symptoms. In our two random effects Bayesian hierarchical models, we find estimated seroprevalences of 8.76% and 9.72%, significantly higher than the 3.3% found by a previous meta-analysis of these eight studies. We also produce a variation of this meta-analysis where we exclude two of the eight studies. In this model, we find an estimated seroprevalence of 4.4%, much lower than our first two Bayesian hierarchical models. We believe a random effects model more accurately reflects the heterogeneity between studies and thus asymptomatic Ebola is more seroprevalent than previously believed among subjects with household exposure or known case-contact. However, a strong conclusion cannot be reached on the seriousness of asymptomatic Ebola without an international testing standard and more data collection using this adopted standard.
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Rivers, Derick Lorenzo. "Dynamic Bayesian Approaches to the Statistical Calibration Problem." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3599.

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The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs the use of Bayesian methodology to perform statistical calibration. The DLM framework is used to capture the time-varying parameters that may be changing or drifting over time. Dynamic based approaches to the linear, nonlinear, and multivariate calibration problem are presented in this dissertation. Simulation studies are conducted where the dynamic models are compared to some well known "static'" calibration approaches in the literature from both the frequentist and Bayesian perspectives. Applications to microwave radiometry are given.
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陳潔妍 and Kit-yin Chan. "Bayesian analysis of wandering vector models for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31214939.

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Chan, Kit-yin. "Bayesian analysis of wandering vector models for ranking data /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19977025.

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Books on the topic "Bayesian statistical analysi"

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Andrew, Gelman, ed. Bayesian data analysis. Boca Raton, FL: Chapman & Hall/CRC, 2000.

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Andrew, Gelman, ed. Bayesian data analysis. London: Chapman & Hall, 1995.

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Bayesian analysis of linear models. New York: M. Dekker, 1985.

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Box, George E. P., and George C. Tiao. Bayesian Inference in Statistical Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1992. http://dx.doi.org/10.1002/9781118033197.

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Box, George E. P. Bayesian inference in statistical analysis. New York: Wiley, 1992.

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Carlin, Bradley P. Bayesian methods for data analysis. 3rd ed. Boca Raton: CRC Press, 2009.

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Carlin, Bradley P. Bayesian methods for data analysis. 3rd ed. Boca Raton: Chapman & Hall/CRC, 2008.

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Carlin, Bradley P. Bayesian methods for data analysis. 3rd ed. Boca Raton: CRC Press, 2009.

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A, Waller Ray, ed. Bayesian reliability analysis. Malabar, Fla: R.E. Krieger Pub. Co., 1991.

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Gomez-Rubio, Virgilio. Bayesian Inference with INLA. Boca Raton, Florida, USA: Taylor & Francis Group, 2020.

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Book chapters on the topic "Bayesian statistical analysi"

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Klugman, Stuart A. "Bayesian Statistical Analysis." In Bayesian Statistics in Actuarial Science, 5–16. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-017-0845-6_2.

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Berger, James O. "Bayesian Analysis." In Springer Series in Statistics, 118–307. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4757-4286-2_4.

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Pardo, Scott. "Bayesian Analyses." In Statistical Analysis of Empirical Data, 161–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43328-4_12.

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Karagiannis, Georgios P. "Introduction to Bayesian Statistical Inference." In Uncertainty in Engineering, 1–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_1.

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AbstractWe present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We present tools for parametric and predictive inference, and particularly the design of point estimators, credible sets, and hypothesis tests. These concepts are presented in running examples. Supplementary material is available from GitHub.
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Crowder, M. J., A. C. Kimber, R. L. Smith, and T. J. Sweeting. "The Bayesian approach." In Statistical Analysis of Reliability Data, 117–36. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2953-2_6.

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Diaconis, Persi. "Bayesian Numerical Analysis." In Statistical Decision Theory and Related Topics IV, 163–75. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_20.

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Pitchforth, Jegar O., and Kerrie L. Mengersen. "Bayesian Meta-Analysis." In Case Studies in Bayesian Statistical Modelling and Analysis, 118–40. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118394472.ch7.

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Yang, Harry, and Steven J. Novick. "Basics of Bayesian Statistics." In Bayesian Analysis with R for Drug Development, 17–40. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315100388-2.

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Basu, D. "Learning Statistics from Counter Examples: Ancillary Statistics." In Bayesian Analysis in Statistics and Econometrics, 217–23. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2944-5_13.

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Díaz, Carmen, Inmaculada de la Fuente, and Carmen Batanero. "Implications between learning outcomes in elementary bayesian inference." In Statistical Implicative Analysis, 163–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78983-3_8.

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Conference papers on the topic "Bayesian statistical analysi"

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Paredes-Cancino, Cristian, and Gisela Montiel-Espinosa. "Analysis of Tasks on Bayes’ Theorem in High School Textbooks." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t14g2.

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The aim of this study is to determine the characteristics of the tasks on Bayes' Theorem in textbooks. In order to develop the research, a framework has been constructed consisting of theoretical elements in relation to the representation and format of statistical information, epistemological considerations, and the contexts associated with Bayesian statistics. The study is carried out following the methodology of content analysis in a sample of textbooks at the high school level in Mexico. The analysis shows a tendency of tasks to develop procedural knowledge, static character management, use of the principles of classical and frequent quantification, and centrality in the tree diagram as a representation for the resolution of Bayesian problems. El objetivo del estudio es determinar las características de las tareas sobre el Teorema de Bayes en los libros de texto. Para desarrollar la investigación se ha construido un marco conformado por elementos teóricos en relación con la representación y el formato de la información estadística, consideraciones epistemológicas y los contextos asociados a la estadística bayesiana. El estudio se realiza siguiendo la metodología del análisis de contenido en una muestra de libros de textos escolares del nivel bachillerato en México. El análisis evidencia una tendencia de las tareas al desarrollo de conocimiento procedimental, manejo del carácter estático, empleo de los principios de cuantificación clásico y frecuencial y la centralidad en el diagrama de árbol como representación para la resolución de problemas bayesianos.
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STERN, R. B., and C. A. de B. PEREIRA. "STATISTICAL INFORMATION: A BAYESIAN PERSPECTIVE." In Proceedings of the 7th International Workshop on Data Analysis in Astronomy “Livio Scarsi and Vito DiGesù”. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814383295_0001.

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Begriche, Youcef, and Ahmed Serhrouchni. "Bayesian statistical analysis for spams." In 2010 IEEE 35th Conference on Local Computer Networks (LCN 2010). IEEE, 2010. http://dx.doi.org/10.1109/lcn.2010.5735846.

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Pires, Rubiane Maria, and Carlos Alberto Ribeiro Diniz. "Bayesian residual analysis for beta-binomial regression models." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759610.

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Rodrigues, Erica Castilho, and Renato Assunção. "Multiple neighborhood model for analysis of SIDS space-time data set." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759612.

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Miles, Paul, Adriane Guettler, Yousuff Hussaini, and William S. Oates. "Uncertainty Analysis of Dielectric Elastomer Membranes Under Multi-Axial Loading." In ASME 2015 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/smasis2015-8919.

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A variety of models have been developed to simulate the behavior of electroactive elastomers. As with all modeling applications, there are varying levels of uncertainty associated with measurement limitations and lack of knowledge of the constitutive behavior. Methods of quantifying this uncertainty have been explored previously using Bayesian statistics under uniaxial mechanical loading. The research presented here expands prior developments to quantify constitutive model uncertainty under multi-axial mechanical loading at different electrostatic fields. Specifically, we experimentally characterize and simulate transverse loading of a pre-stretched membrane under different electrostatic fields. We also quantify the dielectric response from electric displacement versus electric field loops. Bayesian statistical methods are employed to quantify modeling uncertainties in light of the data conducted on the 3M elastomer, VHB 4910.
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Ray, Priyadip, and Lawrence Carin. "Nonparametric Bayesian factor analysis of multiple time series." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967742.

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COWAN, GLEN. "BAYESIAN STATISTICAL METHODS FOR PARTON ANALYSES." In Proceedings of the 14th International Workshop. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812706706_0030.

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Kobayashi, Kei, Orita Mitsuru, and Henry P. Wynn. "Statistical analysis via the curvature of data space." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING (MAXENT 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4905968.

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Meng, Jia, Jianqiu Zhang, Yidong Chen, and Yufei Huang. "Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967704.

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Reports on the topic "Bayesian statistical analysi"

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Tian, Cong, Jianlong Shu, Wenhui Shao, Zhengxin Zhou, Huayang Guo, and Jingang Wang. The efficacy and safety of IL Inhibitors, TNF-α Inhibitors, and JAK Inhibitor on ankylosing spondylitis: A Bayesian network meta-analysis of a “randomized, double-blind, placebo-controlled” trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0117.

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Review question / Objective: In this study, we conducted a Bayesian network meta-analysis to evaluate the efficacy and safety of interleukin (IL) inhibitors, tumor necrosis factor-alpha (TNF-α) inhibitors, and Janus kinase (JAK) inhibitors on ankylosing spondylitis (AS).The purpose of this study is to compare the effectiveness and safety of different interventions for treating AS to provide insights into the decision-making in clinicalpractice. Condition being studied: Ankylosing spondylitis. Based on the Bayesian hierarchical model, we conducted a network meta-analysis using the gemtc package in R software (version 4.1.3) and Stata software (version 15.1). Cong Tian and Jianlong Shu contributed to the conception and design of the study and supervised the tweet classification. All authors drafted the manuscript. Wenhui Shao, Zhengxin Zhou, Huayang Guo and Jingang Wang contributed to data management and tweet classification. Cong Tian, Jianlong Shu and Zhengxin Zhou performed the statistical analysis. Cong Tian, Jianlong Shu, Wenhui Shao and Zhengxin Zhou reviewed the manuscript.
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Zhao, Binghao, Yu Wang, and Wenbin Ma. Comparative Efficacy and Safety of Therapeutics for Elderly Glioblastoma: a Bayesian Network Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0094.

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Review question / Objective: At this time, a comprehensive systematic review and network meta-analysis (NMA) was conducted to: (1) fill the research gap by giving rankings on treatment efficacy; (2) provide statistical evidence of not head-to-head comparisons; (3) seek out the best and up-to-date therapeutic strategy reported in latest RCTs; (4) address potential adverse events (AEs) of available treatments. Condition being studied: The incidence of glioblastoma (GBM) increases with age, until now, there has been less evidence on the optimal treatments for elderly GBM since only general GBM populations were included in clinical trials. Given the poor survival of elderly GBM, we collected randomized controlled trials about newly diagnosed GBM (ndGBM) and recurrent GBM, and conducted a Bayesian network meta-analysis on ndGBM regarding overall survival (OS) and progression-free survival (PFS). We revealed TTF + TMZ and TMZ + HFRT were likely to be best treatments for OS; BEV + HFRT and TMZ + HFRT were likely to be best options for PFS. Current study is the most comprehensive and powered network analysis on elderly GBM until now, it also provides more insights for elderly GBM management.
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Clausen, Jay, Christopher Felt, Michael Musty, Vuong Truong, Susan Frankenstein, Anna Wagner, Rosa Affleck, Steven Peckham, and Christopher Williams. Modernizing environmental signature physics for target detection—Phase 3. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43442.

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.
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