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Book Synopsis

1 Why R is essential? What are the prospects by learning R?.- 2 An overview of statistical analysis plan for clinical studies.- 3 Introduction to R environment and basic commands.- 4 Data handling and manipulation in R with Descriptive Statistics.- 5 Introduction to packages in R installation, loading, unloading and deletion.- 6 Visualisation of data basic and advanced.- 7 Inferential statistics for the hypothesis testing of parametrically distributed data.- 8 Inferential statistics for the hypothesis testing of  non-parametric data.- 9 Computation of sample size for clinical studies.- 10 Correlation and linear regression analysis for continuous outcome.- 11 Logistic regression analysis for categorical outcome.- 12 Receiver Operating Characteristic (ROC) curve analysis for diagnostic studies.- 13 Survival analysis for time to event-based outcome.- 14 Conducting randomization in clinical trials.- 15 Development of web-based interactive servers using R shiny package.

R for Basic Biostatistics in Medical Research

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    A Hardback by Anand Srinivasan

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      Publisher: Springer
      Publication Date: Publication Date: 1/10/2025
      ISBN13: 9789819769797, 978-9819769797
      ISBN10: 9819769795

      Description

      Book Synopsis

      1 Why R is essential? What are the prospects by learning R?.- 2 An overview of statistical analysis plan for clinical studies.- 3 Introduction to R environment and basic commands.- 4 Data handling and manipulation in R with Descriptive Statistics.- 5 Introduction to packages in R installation, loading, unloading and deletion.- 6 Visualisation of data basic and advanced.- 7 Inferential statistics for the hypothesis testing of parametrically distributed data.- 8 Inferential statistics for the hypothesis testing of  non-parametric data.- 9 Computation of sample size for clinical studies.- 10 Correlation and linear regression analysis for continuous outcome.- 11 Logistic regression analysis for categorical outcome.- 12 Receiver Operating Characteristic (ROC) curve analysis for diagnostic studies.- 13 Survival analysis for time to event-based outcome.- 14 Conducting randomization in clinical trials.- 15 Development of web-based interactive servers using R shiny package.

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