Biostatistics · Categorical Data Analysis in Health · Introduction

Introduction

What this guide covers

In clinical, epidemiological, and public health research, most outcomes of interest are not continuous quantitative variables. Diagnosing a pathology, a patient's death, complete tumor remission, or the degree of satisfaction with a treatment are inherently categorical constructs. The rigorous statistical analysis of these data requires a formal probabilistic framework.

Overview

Topics covered

Introduction
Analysis of categorical health data
Nature of categorical data in clinical research
Measurement scales and biomedical classification
Underlying probabilistic models
Measures of association and strength of effect on health

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

\P(Y = y) = \binom{n}{y} p^y (1-p)^{n-y}, \qquad y \in \{0, 1, \dots, n\}

View LaTeX source

\P(Y = y) = \binom{n}{y} p^y (1-p)^{n-y}, \qquad y \in \{0, 1, \dots, n\}