Target Participants

This course is suitable for epidemiologists and biostatisticians and health researchers including clinicians, laboratory and social scientists.

What you will learn

By the end of the course the learner should be able to:

• Describe key concepts in epidemiology and biostatistics
• Describe descriptive, analytic and experimental study designs
• Describe and summarize data
• Develop hypothesis and analyze data while applying appropriate statistical methods in research
• Interpret and communicate results

Course duration

5 days

Course Outline

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
• Survey design
• 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
• Causality – Guidelines for inferring causation: Bradford Hill Criteria

Confounding & Interaction:

• Confounding and Interaction,
• Control of Confounding (Restriction, Matching, Stratification)
• Control of Confounding (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 to STATA:

• Definitions
• Types of data
• Data management (data entry, importing & exporting data and generation & recoding of variables)
• Simple descriptive summaries of data including graphical displays
• 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

Stratified analysis of 2 by 2 tables:

• Mantel-Haenszel Chi-square and Odds ratio
• Stratified analysis

Analysis of continuous variables:

• T-tests
• Analysis of Variance (ANOVA)
• Simple linear & multiple linear regression.

Survival Analysis

• Analysis of duration outcomes
• Estimating the probability of survival
• Modeling survival as a function of covariates using Cox, Weibull, lognormal, and other regression models.
• Predict hazard ratios
• Exercises