Epidemiology & Biostatistics

Epidemiology & Biostatistics


To equip learners with knowledge and skills in basic principles and methods of epidemiology and biostatistics and their application in public health and clinical practice

Learning Objectives

  1. Describe key concepts in epidemiology
  2. Describe descriptive, analytic and experimental study designs
  3. Outline methods of data analysis
  4. Apply appropriate statistical methods in research


Epidemiology: Introduction to Epidemiology: Definitions, concepts & uses of Epidemiology, Descriptive & Analytic Epidemiology, Historical developments in Epidemiology; Measures of disease frequency: Count, proportion, odds, rate, Incidence risk & rate, Prevalence, Relationship between incidence & prevalence, Other measures of disease frequency; Measures of association: Measures of association (Incidence risk/rate ratio, Relative risk, Odds ratio, Measures of effect in the exposed group (Attributable risk, Attributable fraction), Measures of effect in the population (Population attributable risk, Population attributable fraction); Epidemiological study designs: Cross sectional studies, Ecological studies; Epidemiological study designs: Case control studies, Cohort studies, interventional studies; Sampling: Non-probability sampling (convenience, judgment, purposive), Probability sampling (Simple random, Systematic random, Stratified random, multistage), Sample size determination & sampling to detect disease; Bias: Selection bias, Information bias, Confounding bias, Reading Assignment (Causality – Guidelines for inferring causation: Bradford Hill Criteria); Confounding & Interaction: Confounding and Interaction, Control of Confounding (Restriction, Matching, Stratification, Standardisation, Statistical modelling); Screening & Diagnostic tests: Definitions, Disease eligibility for screening, Accuracy, precision & agreement, True and apparent prevalence, Predictive values, Receiver operating characteristic curves.

Biostatistics: Introduction: Definitions, types of data, Descriptive statistics for quantitative & qualitative variables; Probability distributions: Normal, Binomial, Poisson; Inferential statistics: Sampling variability of a mean, Sampling variability of a proportion; Decision errors (Type I & II), Comparing two means and two proportions: Confidence intervals, Hypothesis testing, Paired-t-test for paired data; Association between two categorical variables: Chi-square test, Chi-square test for trend, McNemar’s test for paired proportions, Introduction to computer analysis: Data management (data entry, importing & exporting data and generation & recoding of variables), Simple descriptive summaries of data including graphical displays; Stratified analysis of 2 by 2 tables: Mantel-Haenszel Chi-square and Odds ratio, Stratified analysis; Computer analysis of continuous variables: T-tests, Analysis of Variance (ANOVA), Simple linear & multiple linear regression.