Introduction to Biostatistics

What is Biostatistics?

• Biostatistics deals with the collection, classification, analysis, and interpretation of biological and biomedical data.
• It is a specialized area of statistics focusing on health-related data.

Populations and Samples

• Population characteristics are not known but can be inferred from the sample.
• Sample characteristics are known and can be measured.
• To make accurate inferences, appropriate sample selection and accurate measurement are crucial to avoid bias.

Biostatistics in Medicine

• It helps generate medical knowledge by understanding natural and experimental observations in science and medicine.
• It assists in designing, managing, and analyzing clinical trials.
• Biostatistics helps to determine how diseases develop, progress, and spread.

The Steps of Clinical Research

• The steps include study design, data collection, data processing, data analysis, and output results.
• Statistical software, such as R, can be used for data analysis.

Reproducible Research

• A research study should be reproducible to ensure the reliability of the results.
• Code should be used throughout the steps of a research study, from data processing to results presentation.

Data in Tabular Form

• Data is often presented in a tabular (rectangular) format.
• Each row represents an observation, while each column represents a variable.
• A primary key uniquely identifies each observation.

Types of Variables

• Variables can be categorized as categorical (non-numerical) or numerical.
• Categorical variables can be nominal (without inherent ordering), ordinal (with inherent ordering), or dichotomous (binary).
• Numerical variables can be continuous (measurement on a continuous scale) or discrete (limited number of discrete values).

Different Types of Variables Require Different Statistical Approaches

• Statistical methods used for analysis depend on the type of variable.

Data Conversion

• Variables can be converted between different types:
  • Numerical variables can be converted into categorical variables (e.g., age to age group).
  • Categorical variables can be converted into numerical variables (e.g., class to length).
• “NA” represents missing values in R.

Vector Indexing

• Indexing in R allows selecting elements from a vector or other objects using the indexing operator [ ].
• There are four ways to use the indexing operator [ ].
  • Positive integer vector: Specifies the positions of the elements to keep.
  • Negative integer vector: Specifies positions of the elements to remove.
  • Character vector: Identifies elements by their names.
  • Logical vector: Selects elements based on a condition.