In medical research, understanding different types of statistical data is crucial for accurate analysis. The main types are:

1. Quantitative Variables:

Quantitative variables are essential in medical data analysis, representing numeric values used for arithmetic operations. They offer nuanced insights into various clinical parameters. Examples include:

Age
Blood Pressure
BMI (Body Mass Index)
Pulse Rate
Temperature
Laboratory Values
Weight and Height
GFR (Glomerular Filtration Rate)

2. Categorical Variables:

Categorical variables classify individuals into distinct groups and include three subclasses:

A. Binary Variables: Characterized by a dualistic classification, such as "Dead or Alive" or "Disease or No Disease."
B. Nominal Variables: Multi-class categorization without inherent order, like patient blood types (O, A, B, AB) or marital statuses (Single, Married, Divorced, Widowed, Separated).
C. Ordinal Variables: Sequential arrangement of categories, embedding a hierarchy, for example, breast cancer staging (I, II, III, IV) or Likert scale ratings (e.g., Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree).

3. Time-to-Event Variables:

Time-to-event variables, a distinctive facet in medical investigations, encompass both continuous and binary attributes. These variables encapsulate events that transpire over time, where not all participants experience the event of interest. A twofold structure distinguishes time-to-event variables:

A. Binary Component: Conveys the occurrence or absence of the event under scrutiny. Instances encompass events like death, heart attacks, or the onset of chronic kidney disease.
B. Continuous Component: The temporal duration preceding the event's manifestation constitutes the continuous dimension.