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
- Describe key concepts in epidemiology
- Describe descriptive, analytic and experimental study designs
- Outline methods of data analysis
- 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.