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   Articles with "regression model" as a keyword



Brain tumor detection and patient survival prediction using U‐Net and regression model

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recommendations! Published in 2022 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22735

Abstract: Brain tumor segmentation is necessitated to ascertain the severity of tumor growth in a brain for possible treatment planning. In this work, we attempt the development of U‐Net‐based semantic segmentation of brain tumors. This network… read more here.

Keywords: tumor; brain tumor; model; regression model ... See more keywords

Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules

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recommendations! Published in 2022 at "Journal of Ultrasound in Medicine"

DOI: 10.1002/jum.16078

Abstract: To explore the potential value of ultrasound radiomics in differentiating between benign and malignant breast nodules by extracting the radiomic features of two‐dimensional (2D) grayscale ultrasound images and establishing a logistic regression model. read more here.

Keywords: malignant breast; logistic regression; breast nodules; ultrasound radiomics ... See more keywords

A Complex Meta‐Regression Model to Identify Effective Features of Interventions From Multi‐Arm, Multi‐Follow‐Up Trials

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recommendations! Published in 2024 at "Statistics in Medicine"

DOI: 10.1002/sim.10237

Abstract: Network meta‐analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In many areas of research, interventions are often complex, made up of multiple components or features. This makes… read more here.

Keywords: features interventions; regression model; regression; model ... See more keywords

“Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies” by Hongtao Zhang, Alan Chiang, and Jixian Wang

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recommendations! Published in 2022 at "Statistics in Medicine"

DOI: 10.1002/sim.9494

Abstract: In their paper, Zhang et al 1 propose further extensions of the Bayesian Logistic Regression Model (BLRM) with overdose control for dose-escalation studies of a novel drug. These extensions aim to reduce the risk of… read more here.

Keywords: oncology; logistic regression; zhang; overdose control ... See more keywords

Commentary on “Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies”

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recommendations! Published in 2022 at "Statistics in Medicine"

DOI: 10.1002/sim.9496

Abstract: The Bayesian logistic regression model (BLRM) design is a variation of the continuous reassessment method (CRM). Due to the use of an excessively tight escalation with overdose control (EWOC) rule, BLRM has high tendency to… read more here.

Keywords: oncology; logistic regression; overdose control; bayesian logistic ... See more keywords

Unified estimation for Cox regression model with nonmonotone missing at random covariates

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recommendations! Published in 2022 at "Statistics in Medicine"

DOI: 10.1002/sim.9512

Abstract: This article investigates a unified estimator for Cox regression model (Cox, 1972) when covariate data are missing at random (Rubin, 1976). It extends the idea of using parametric working models (Zhao and Liu, 2021) to… read more here.

Keywords: cox regression; regression model; cox; unified estimator ... See more keywords

A Bayesian (meta‐)regression model for treatment effects on the risk difference scale

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recommendations! Published in 2023 at "Statistics in Medicine"

DOI: 10.1002/sim.9697

Abstract: In clinical settings, the absolute risk reduction due to treatment that can be expected in a particular patient is of key interest. However, logistic regression, the default regression model for trials with a binary outcome,… read more here.

Keywords: treatment effects; meta; model; treatment ... See more keywords

Bayesian discrete lognormal regression model for genomic prediction

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recommendations! Published in 2024 at "Theoretical and Applied Genetics"

DOI: 10.1007/s00122-023-04526-4

Abstract: Genomic prediction models for quantitative traits assume continuous and normally distributed phenotypes. In this research, we proposed a novel Bayesian discrete lognormal regression model. Genomic selection is a powerful tool in modern breeding programs that… read more here.

Keywords: regression model; lognormal regression; model; genomic prediction ... See more keywords
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Statistical inference for the functional quadratic quantile regression model

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recommendations! Published in 2020 at "Metrika"

DOI: 10.1007/s00184-020-00763-5

Abstract: In this paper, we develop statistical inference procedures for functional quadratic quantile regression model in which the response is a scalar and the predictor is a random function defined on a compact set of R… read more here.

Keywords: quantile regression; regression model; functional quadratic; model ... See more keywords

Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model

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recommendations! Published in 2017 at "Statistical Papers"

DOI: 10.1007/s00362-015-0683-1

Abstract: The zero inflated hyper-Poisson regression model permits count data to be analysed with covariates that determine different levels of dispersion and that present structural zeros due to the existence of a non-users group. A simulation… read more here.

Keywords: regression model; zero inflated; model; dispersion ... See more keywords

A regression model for overdispersed data without too many zeros

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recommendations! Published in 2017 at "Statistical Papers"

DOI: 10.1007/s00362-015-0724-9

Abstract: A regression model for overdispersed count data based on the complex biparametric Pearson (CBP) distribution is developed. It is compared with the generalized Poisson regression model, the negative binomial regression model and the zero inflated… read more here.

Keywords: cbp distribution; model overdispersed; regression; regression model ... See more keywords