Big Data Clinical Study and Its Implementation in R

Zhongheng Zhang (Editor), Fionn Murtagh (Editor), Sven Van Poucke (Editor)

Research output: Book/ReportBook

Abstract

With the increasing availability of big data, the need is urgent for more studies of best practices when dealing with these data. There are six chapters in this book. Chapter 1 provides an overview of the big data clinical research, including the perspective, the general accessing workflow, a brief review of machine learning methods and data acquisition and management. Chapter 2 discusses about exploratory data analysis and data management. It focuses on the missing data problem that is frequently encountered in clinical studies by introducing a number of methods and their applications. First it discusses about missing data exploration and data reshaping and aggregating. Then it introduces several imputation methods including single imputation, multiple imputation, and multivariate imputation. Chapter 3 discusses methods for variable selection for both parametric and non-parametric models that are commonly used in clinical studies. It also discusses about methods for diagnostic and introduced a useful R package to draw Nomograms. Chapter 4 discusses about the analysis of survival data. In this chapter both the application of parametric and semi-parametric models are illustrated, as well as the competing risk model. Chapter 5 discusses several commonly used unsupervised and supervised machine learning methods including the k nearest neighbor, naïve Bayes classification, decision tree and neural network. Chapter 6 addresses a number of other important statistical areas that has applications in clinical studies, for example, the hierarchical cluster analysis and its visualization with R, causal mediation analysis, structural equation modeling, and case-crossover design.
LanguageEnglish
Place of PublicationHong Kong
PublisherAME Publishing Company
Number of pages233
ISBN (Print)9789887784081
Publication statusPublished - 2018

Fingerprint

Imputation
Data Management
Missing Data
Machine Learning
Nomogram
Competing Risks Model
Crossover Design
Exploratory Data Analysis
Tree Networks
Multiple Imputation
Structural Equation Modeling
Naive Bayes
Mediation
Survival Data
Best Practice
Semiparametric Model
Nonparametric Model
Supervised Learning
Cluster Analysis
Variable Selection

Cite this

Zhang, Z., Murtagh, F., & Van Poucke, S. (Eds.) (2018). Big Data Clinical Study and Its Implementation in R. Hong Kong: AME Publishing Company.
Zhang, Zhongheng (Editor) ; Murtagh, Fionn (Editor) ; Van Poucke, Sven (Editor). / Big Data Clinical Study and Its Implementation in R. Hong Kong : AME Publishing Company, 2018. 233 p.
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Zhang, Z, Murtagh, F & Van Poucke, S (eds) 2018, Big Data Clinical Study and Its Implementation in R. AME Publishing Company, Hong Kong.

Big Data Clinical Study and Its Implementation in R. / Zhang, Zhongheng (Editor); Murtagh, Fionn (Editor); Van Poucke, Sven (Editor).

Hong Kong : AME Publishing Company, 2018. 233 p.

Research output: Book/ReportBook

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Zhang Z, (ed.), Murtagh F, (ed.), Van Poucke S, (ed.). Big Data Clinical Study and Its Implementation in R. Hong Kong: AME Publishing Company, 2018. 233 p.