scholarly journals On the Accuracy of the Sine Power Lomax Model for Data Fitting

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 78-104
Author(s):  
Vasili B. V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Statistical Model ◽  
Simulation Study ◽  
Statistical Models ◽  
Goodness Of Fit ◽  
Data Fitting ◽  
Applied Science ◽  
Survival Distribution ◽  
Lomax Distribution ◽  
Model Based

Every day, new data must be analysed as well as possible in all areas of applied science, which requires the development of attractive statistical models, that is to say adapted to the context, easy to use and efficient. In this article, we innovate in this direction by proposing a new statistical model based on the functionalities of the sinusoidal transformation and power Lomax distribution. We thus introduce a new three-parameter survival distribution called sine power Lomax distribution. In a first approach, we present it theoretically and provide some of its significant properties. Then the practicality, utility and flexibility of the sine power Lomax model are demonstrated through a comprehensive simulation study, and the analysis of nine real datasets mainly from medicine and engineering. Based on relevant goodness of fit criteria, it is shown that the sine power Lomax model has a better fit to some of the existing Lomax-like distributions.

Diffusion-weighted imaging of prostate cancer using a statistical model based on the gamma distribution

2014 ◽  
Vol 42 (1) ◽  
pp. 56-62 ◽  
Author(s):  
Hiroshi Shinmoto ◽  
Koichi Oshio ◽  
Chiharu Tamura ◽  
Shigeyoshi Soga ◽  
Teppei Okamura ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Statistical Model ◽  
Gamma Distribution ◽  
Diffusion Weighted Imaging ◽  
Model Based ◽  
Diffusion Weighted

scholarly journals Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

2021 ◽  
Vol 18 (1) ◽  
pp. 345
Author(s):  
Pavel Mozgunov ◽  
Rochelle Knight ◽  
Helen Barnett ◽  
Thomas Jaki
Keyword(s):  
Phase I ◽  
Interaction Parameter ◽  
Simulation Study ◽  
Small Sample Size ◽  
Small Sample ◽  
Dose Finding ◽  
Phase I Trials ◽  
Model Based ◽  
Combination Toxicity ◽  
Toxicity Relationship

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

Numerical Simulation Study of Metal Transfer in MIG Welding with Magnetic Control

2014 ◽  
Vol 532 ◽  
pp. 545-548 ◽  
Author(s):  
Chao Yang ◽  
Shu Yuan Jiang ◽  
Hai Bo Bi
Keyword(s):  
Fluid Dynamics ◽  
Numerical Simulation ◽  
Mathematical Model ◽  
Simulation Study ◽  
Electromagnetic Theory ◽  
Metal Transfer ◽  
Experimental Result ◽  
Magnetic Control ◽  
Mig Welding ◽  
Model Based

This paper simulate the mode of metal transfer in MIG magnetic control welding by using CFD software FLUENT, establishing mathematical model based on fluid dynamics and electromagnetic theory, and simulate the form, grow and drop process of metal transfer with and without magnetic. Meanwhile, do experiments to confirm the simulate result, and it is well consistent with the experimental result.

scholarly journals Exploring a model-based analysis of patient derived xenograft studies in oncology drug development

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10681
Author(s):  
Jake Dickinson ◽  
Marcel de Matas ◽  
Paul A. Dickinson ◽  
Hitesh B. Mistry
Keyword(s):  
Simulation Study ◽  
Statistical Power ◽  
Tumour Growth ◽  
Empirical Approach ◽  
Series Data ◽  
Ratio Test ◽  
Data Set ◽  
Model Based ◽  
Patient Derived Xenograft ◽  
Model Based Analysis

Purpose To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs. Methods Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX). Results The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI. Conclusions The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.

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