Advanced Biostatistics & Statistical Modelling for Public Health Training Workshop/Seminar

Course overview

The Advanced Biostatistics & Statistical Modelling for Public Health Training Workshop equips public health professionals, epidemiologists, biostatisticians and researchers with advanced statistical techniques for analysing health data, evaluating interventions and generating reliable research evidence. The training covers multivariable regression, survival analysis, longitudinal data analysis, multilevel modelling, causal inference, epidemiological study design, statistical hypothesis testing and time-series analysis. Participants learn to select appropriate statistical methods, assess model assumptions, interpret statistical results and communicate findings for public health policy, programme evaluation and clinical or population health research. Practical sessions use standard statistical software such as R, SPSS, Stata, SAS and Excel, depending on institutional requirements and participant experience.

Master advanced biostatistics, regression, survival analysis, causal inference and statistical modelling for public health research.

Course objectives

• Apply advanced biostatistical methods to epidemiological, clinical and population health datasets.
• Produce reproducible statistical analyses, research reports and evidence-based recommendations for public health decision-making.
• Interpret statistical significance, confidence intervals, effect sizes and model performance in context.
• Apply causal inference methods and evaluate public health interventions using observational and experimental data.
• Assess model assumptions, confounding, interaction effects, statistical uncertainty and potential sources of bias.
• Conduct survival analysis, longitudinal data analysis and multilevel statistical modelling.
• Develop and interpret multivariable regression models for public health research.
• Select appropriate statistical tests and modelling techniques based on research questions, study designs and data characteristics.

Who should attend

Course content

• Module 1: Advanced Biostatistics and Statistical Inference Foundations of biostatistical analysis, probability distributions, sampling distributions, estimation theory, confidence intervals, hypothesis testing, statistical power, sample size determination
• Module 2: Epidemiological Study Design and Statistical Analysis Cross-sectional studies, cohort studies, case-control studies, randomised controlled trials, ecological studies, repeated-measures designs, observational versus experimental research, outcome
• Module 3: Data Quality, Preparation and Exploratory Analysis Health data quality assessment, missing data patterns, outlier detection, data coding and validation, descriptive statistics, distribution assessment, exploratory data analysis, variable transfo
• Module 4: Advanced Probability and Distribution Models Normal and non-normal distributions, binomial and Poisson distributions, negative binomial distributions, sampling variability, probability modelling, distribution selection, assumptions underlying st
• Module 5: Multivariable Linear Regression Simple and multiple linear regression, regression coefficients, model specification, categorical predictors, interaction terms, confounding control, residual analysis, multicollinearity, model fit, influential obs
• Module 6: Logistic and Generalised Linear Models Binary logistic regression, multinomial and ordinal logistic regression, Poisson regression, negative binomial regression, count data analysis, log-binomial and modified Poisson approaches to estimating rel
• Module 7: Survival Analysis and Time-to-Event Modelling Time-to-event data, censoring, Kaplan–Meier survival curves, log-rank tests, Cox proportional hazards regression, hazard ratios, proportional hazards assumptions, competing risks, survival probabil
• Module 8: Longitudinal and Repeated-Measures Data Analysis Longitudinal study designs, repeated observations, within-person correlation, generalised estimating equations, mixed-effects models, random and fixed effects, changes over time, handling incomple
• Module 9: Multilevel and Hierarchical Modelling Hierarchical data structures, individuals nested within households or communities, patients nested within health facilities, random intercepts and slopes, intraclass correlation, contextual effects, cluster-
• Module 10: Causal Inference and Confounding Control Causal questions and estimands, directed acyclic graphs, confounding and mediation, selection bias, propensity score methods, matching and weighting, standardisation, inverse probability weighting, sensi
• Module 11: Missing Data and Statistical Imputation Types and mechanisms of missing data, missing completely at random, missing at random and missing not at random, complete-case analysis, multiple imputation, sensitivity analysis, assessing the impact of
• Module 12: Time-Series Analysis in Public Health Time-dependent health data, trend and seasonal analysis, autocorrelation, stationarity, interrupted time-series designs, autoregressive models, forecasting, seasonal disease patterns, public health interven
• Module 13: Bayesian Biostatistics and Statistical Modelling Bayesian statistical principles, prior and posterior distributions, likelihood functions, Bayesian estimation, credible intervals, prior selection, posterior interpretation, Bayesian regression,
• Module 14: Statistical Model Diagnostics and Validation Assessing model assumptions, residual diagnostics, goodness-of-fit tests, multicollinearity assessment, influential observations, overfitting, internal and external validation, cross-validation, disc
• Module 15: Survey Data Analysis and Complex Sampling Probability sampling, stratification, cluster sampling, sampling weights, design effects, weighted estimates, variance estimation, complex survey regression, non-response adjustment, population-level in
• Module 16: Clinical Trials and Public Health Intervention Evaluation Randomisation and allocation, treatment effect estimation, intention-to-treat analysis, per-protocol analysis, baseline adjustment, subgroup analysis, interaction testing, cluster-random
• Module 17: Statistical Software and Research Reporting Applying statistical methods using R, SPSS, Stata or SAS, selecting software based on institutional needs, importing and validating data, running statistical procedures, interpreting software output,
• Module 18: Capstone Project and Applied Public Health Research Formulating a statistical research question, selecting a suitable public health dataset, developing an analysis plan, selecting statistical models, conducting diagnostic checks, interpreting e

General notes

• Participants will work with sample or appropriately de-identified public health datasets to apply the methods covered during the workshop. Descriptive analysis exercise — statistical summary and data quality report, regression analysis exercise — multivariable model with adjusted effect estimates, count data exercise — disease incidence model and interpretation report, survival analysis exercise — Kaplan–Meier curves and hazard ratio interpretation, longitudinal analysis exercise — repeated-measures model and trend assessment, multilevel modelling exercise — analysis of individual and facility-level variation, causal inference exercise — intervention effect estimation with assumptions documented, missing data exercise — imputation and sensitivity analysis report, survey analysis exercise — weighted population estimates, time-series exercise — intervention impact assessment, model diagnostics exercise — model validation and assumption assessment report, capstone research project — complete statistical analysis plan, results tables and public health research report.
• Advanced Biostatistics Course, Statistical Modelling for Public Health, Epidemiological Statistics Training, Survival Analysis Training, Multilevel Modelling, Public Health Research Methods, Advanced Regression Analysis, Clinical Research Statistics, Causal Inference Training, Biostatistics Software Training.

Frequently asked questions

Schedule & fees