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The term Statistics and Data is now familiar to most of us even though we are related to different fields such as Clinical, Biology, Business & Management, Engineering and Physical Sciences. Statistical methods are being applied in different fields including the emerging fields such as Machine Learning, Natural Language Processing and Artificial Intelligence because of the fact that each field deals with data in one form or the other.

Statistical methods help decision makers to arrive at evidence based decisions by collecting, analyzing and interpreting the data. Biostatistics is a branch of statistics which is applied in the biomedical domain and it includes clinical and biological fields.

Biostatistical methods include descriptive, inferential and Statistical modeling tools. Descriptive statistical tools help us to draw information and summary about the data in terms of measures of central tendency, measures of dispersion, measure of skewness and underlying probability distributions. Inferential Statistical tools help us to estimate, test the hypothesis and draw inferences about the parameters of the data. Statistical modeling tools help us to study and estimate the relationship between the variables.

The book starts with the introduction of concepts in biostatistics and moves on to explore descriptive and inferential statistical tools using R Open Source Software. It then explores statistical modeling tools such as regression analysis and survival analysis.

The book also covers the role of Biostatistics in the clinical trials process. Clinical trials are scientific experiments which are conducted in different phases. It involves testing of drugs and clinical procedures for treating different diseases and conditions among a sample of participants before rolling out in the market. Biostatistics tools help clinical trial researchers in deciding about the study design, sample size and analytical methods.

The data set used in the book can be downloaded from Author’s Website.

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Biostatistics explored through r software

  1. 1. Biostatistics explored through R software: An overview Vinaitheerthan Renganathan
  2. 2. Introduction  The term Statistics and Data is now familiar to most of us even though we are related to different fields such as Clinical, Biology, Business & Management, Engineering and Physical Sciences. Statistical methods are being applied in different fields including the emerging fields such as Machine Learning, Natural Language Processing and Artificial Intelligence because of the fact that each field deals with data in one form or the other.  Statistical methods help decision makers to arrive at evidence based decisions by collecting, analyzing and interpreting the data. Biostatistics is a branch of statistics which is applied in the biomedical domain and it includes clinical and biological fields.
  3. 3. Introduction  Biostatistical methods include descriptive, inferential and Statistical modeling tools. Descriptive statistical tools help us to draw information and summary about the data in terms of measures of central tendency, measures of dispersion, measure of skewness and underlying probability distributions. Inferential Statistical tools help us to estimate, test the hypothesis and draw inferences about the parameters of the data. Statistical modeling tools help us to study and estimate the relationship between the variables.  The book starts with the introduction of concepts in biostatistics and moves on to explore descriptive and inferential statistical tools using R Open Source Software. It then explores statistical modeling tools such as regression analysis and survival analysis.
  4. 4. Introduction  The book also covers the role of Biostatistics in the clinical trials process. Clinical trials are scientific experiments which are conducted in different phases. It involves testing of drugs and clinical procedures for treating different diseases and conditions among a sample of participants before rolling out in the market. Biostatistics tools help clinical trial researchers in deciding about the study design, sample size and analytical methods.  The data set used in the book can be downloaded from Author’s Website.
  5. 5. Contents  Chapter 1: Introduction  Chapter 2: R Software  Chapter 3: Descriptive Statistics  Chapter 4: Probability and Probability Distributions  Chapter 5: Sampling Methodology and sampling distributions  Chapter 6: Estimation and Statistical Inference  Chapter 7: Correlation and Regression analysis  Chapter 8: Survival Analysis  Chapter 9: Biostatistics in Randomized Clinical Trials (RCT)  Chapter 10: Meta-Analysis and Systematic reviews
  6. 6. Details  Title : Biostatistics explored through R software: An overview  ISBN: 9789354936586  www.vinaitheerthan.com/book.php  https://www.amazon.com/dp/B09FY5KFYG
  7. 7. Book

The term Statistics and Data is now familiar to most of us even though we are related to different fields such as Clinical, Biology, Business & Management, Engineering and Physical Sciences. Statistical methods are being applied in different fields including the emerging fields such as Machine Learning, Natural Language Processing and Artificial Intelligence because of the fact that each field deals with data in one form or the other. Statistical methods help decision makers to arrive at evidence based decisions by collecting, analyzing and interpreting the data. Biostatistics is a branch of statistics which is applied in the biomedical domain and it includes clinical and biological fields. Biostatistical methods include descriptive, inferential and Statistical modeling tools. Descriptive statistical tools help us to draw information and summary about the data in terms of measures of central tendency, measures of dispersion, measure of skewness and underlying probability distributions. Inferential Statistical tools help us to estimate, test the hypothesis and draw inferences about the parameters of the data. Statistical modeling tools help us to study and estimate the relationship between the variables. The book starts with the introduction of concepts in biostatistics and moves on to explore descriptive and inferential statistical tools using R Open Source Software. It then explores statistical modeling tools such as regression analysis and survival analysis. The book also covers the role of Biostatistics in the clinical trials process. Clinical trials are scientific experiments which are conducted in different phases. It involves testing of drugs and clinical procedures for treating different diseases and conditions among a sample of participants before rolling out in the market. Biostatistics tools help clinical trial researchers in deciding about the study design, sample size and analytical methods. The data set used in the book can be downloaded from Author’s Website.

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