Applied multivariate statistical analysis in medicine
Applied Multivariate Statistical Analysis in Medicine provides a multivariate conceptual framework that allows readers to understand the interconnectivity and interrelations among variables, which maintains the intrinsic precision of statistical theories. With a strong focus on the fundamental conce...
I tiakina i:
| Kaituhi matua: | |
|---|---|
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
London ; San Diego, CA :
Academic Press,
2024.
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| Ngā marau: | |
| Urunga tuihono: | ScienceDirect |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
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| 008 | 240828s2024 enk o 000 0 eng d | ||
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| 020 | |a 9780443235887 |q (electronic bk.) | ||
| 020 | |a 0443235880 |q (electronic bk.) | ||
| 020 | |z 9780443235870 | ||
| 020 | |z 0443235872 | ||
| 035 | |a (OCoLC)1453283051 | ||
| 050 | 4 | |a RA409 | |
| 082 | 0 | 4 | |a 610.2/1 |2 23/eng/202409010 |1 https://id.oclc.org/worldcat/ddc/E3QfhkJdq4p339FKJQ4PbvYdpF |
| 100 | 1 | |a Jiang, Jingmei, |d 1958- |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjx4FjjB44HQMWqHWGwhBP | |
| 245 | 1 | 0 | |a Applied multivariate statistical analysis in medicine |h [electronic resource] / |c Jingmei Jiang. |
| 260 | |a London ; |a San Diego, CA : |b Academic Press, |c 2024. | ||
| 300 | |a 1 online resource | ||
| 505 | 8 | |a 4.6.2 Diagnosis and treatment of outliers -- 4.6.3 Sample size requirement -- 4.7 Summary -- 4.8 Problems -- Bibliography -- 5 -- Generalized linear models -- 5.1 Introduction -- 5.2 Overview of generalized linear models -- 5.2.1 Review of the classical linear regression model -- 5.2.2 Concept of generalized linear models -- 5.2.3 Explanation of model parameters -- 5.3 Data representation of generalized linear models -- 5.3.1 Representation of observational data -- 5.3.2 Quantification of categorical data -- 5.4 Distribution of response variables -- 5.4.1 Exponential family of distributions -- 5.4.2 Mean and variance of distributions in the exponential family -- 5.5 Exponential family and generalized linear models -- 5.5.1 Generalized linear model of non-normal distributed data -- 5.5.2 Generalized linear model of two-point distributed data -- 5.5.3 Generalized linear model of counting data -- 5.6 Parameter estimation for generalized linear models -- 5.6.1 Maximum likelihood estimation -- 5.6.2 Weighted least squares estimation -- 5.7 Hypothesis testing for generalized linear models -- 5.7.1 Likelihood-ratio test -- 5.7.2 Wald test -- 5.7.3 Score test -- 5.7.4 Characteristics and applications of the three statistics -- 5.8 Goodness-of-fit test of generalized linear models -- 5.8.1 Pearson test -- 5.8.2 Deviance test -- 5.9 Application of generalized linear models -- 5.10 Summary -- 5.11 Problems -- Bibliography -- 6 -- Logistic regression -- 6.1 Introduction -- 6.2 Logit behind logistic regression models -- 6.2.1 Probability, odds, and the logarithm of the odds -- 6.2.2 Odds ratio -- 6.3 Binary logistic regression -- 6.3.1 Model definition -- 1 Single predictor models -- 2 Multi-predictor models -- 6.3.2 Parameter estimation of logistic regression -- 1 Maximum likelihood estimation -- 6.3.3 Interpretation of the partial regression coefficient. | |
| 505 | 8 | |a 7.4.5 Parameter estimation and hypothesis testing of the exponential regression model -- 7.5 Weibull model -- 7.5.1 Lifetime functions of the Weibull survival distribution -- 7.5.2 Parameter estimation of the Weibull survival distribution -- 1. For data without censored observations -- 2. For data with censored observations -- 7.5.3 Weibull regression model based on the hazard function -- 7.6 Cox proportional hazard model -- 7.6.1 Basics for the Cox proportional hazard model -- 7.6.2 Partial likelihood -- 7.6.3 Parameter estimation and hypothesis testing using the partial likelihood -- 7.6.4 Applications of the Cox proportional hazard model -- 1. Calculating the hazard ratio -- 2. Calculating the hazard index -- 7.6.5 Assessment of the proportional hazard assumption -- 1. Graphical method -- 2. Checking proportionality with scaled Schoenfield residuals -- 7.7 Extensions to the Cox proportional hazard model -- 7.7.1 Hazard rate model with time-dependent covariates -- 7.7.2 Hazard rate model with repeated events -- 7.8 Summary -- 7.9 Problems -- Bibliography -- 8 -- Principal component analysis -- 8.1 Introduction -- 8.2 Population principal components -- 8.2.1 Understanding principal components -- 8.2.2 Determining principal components -- 8.2.3 Properties of principal components -- 8.2.4 Principal components of standardized variables and properties -- 8.3 Sample principal components -- 8.3.1 Sample principal component scores -- 8.3.2 Determining the number of sample principal components -- 8.4 Steps of principal component analysis -- 8.5 Application of principal component analysis -- 8.5.1 Comprehensive evaluation -- 8.5.2 Principal component regression -- 8.5.3 Variable selection -- 8.5.4 Cluster analysis -- 8.6 Summary -- 8.7 Problems -- Bibliography -- 9 -- Factor analysis -- 9.1 Introduction -- 9.2 Exploratory factor analysis. | |
| 520 | |a Applied Multivariate Statistical Analysis in Medicine provides a multivariate conceptual framework that allows readers to understand the interconnectivity and interrelations among variables, which maintains the intrinsic precision of statistical theories. With a strong focus on the fundamental concepts of multivariate statistical analysis, the book also gives insight into the applications of multivariate distribution in biomedical fields. In 14 chapters, Applied Multivariate Statistical Analysis in Medicine covers the main topics of multivariate analysis methods widely used in health science research. The content is organized progressively from fundamental concepts to sophisticated methods. It begins with basic descriptive statistics in multivariate analysis and follows with parameter estimation, in addition to the hypothesis testing of a multivariate normal distribution, which has heavy applications in biomedical fields where the relationships among approximately normal variables are of great interest. Keeping mathematics to a minimum, considerable emphasis is placed on explanations and real-world applications of core principles to maintain a good balance between introducing theory and cultivating problem-solving skills. This book is a very valuable reference text for clinicians, medical researchers, and other researchers across medical and biomedical disciplines, all of whom confront increasingly complex statistical methods during the analysis and presentation of their results. - Gives understanding and mastering of the multivariate analysis techniques in the medical sciences - Maintains a balance between the introduction of statistical analysis theory and the cultivation of practical skills - Exposes a variety of well-designed real-life cases that integrate concepts and analytical techniques - Includes substantive exercises, online coding sources, and case discussions to solidify a conceptual understanding. | ||
| 650 | 0 | |a Medical statistics. | |
| 650 | 0 | |a Multivariate analysis. | |
| 650 | 2 | |a Multivariate Analysis |0 (DNLM)D015999 | |
| 650 | 6 | |a Analyse multivariée. |0 (CaQQLa)201-0002611 | |
| 650 | 7 | |a Medical statistics |2 fast |0 (OCoLC)fst01014672 | |
| 650 | 7 | |a Multivariate analysis |2 fast |0 (OCoLC)fst01029105 | |
| 776 | 0 | 8 | |i Print version: |z 0443235872 |z 9780443235870 |w (OCoLC)1399165673 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443235870 |