MSc Biostatistics
A specialist postgraduate programme for statisticians entering healthcare, pharmaceutical, and life sciences sectors. With WHO and NHS linkages and regulatory training aligned to FDA and EMA standards, graduates are uniquely prepared to lead statistical evidence generation in clinical and public health settings.
Programme Overview
The MSc Biostatistics is designed for statisticians who want to apply their skills to improve human health. The curriculum covers the full evidence generation pathway: clinical trial design, genomic epidemiology, regulatory statistics, and health technology assessment. Students learn to use R and SAS — the two dominant software environments in pharmaceutical and clinical research — and gain familiarity with ICH E9, FDA, and EMA statistical guidance documents.
Programme links with WHO Geneva, NHS England data teams, and our pharmaceutical industry partners provide unmatched real-world exposure throughout the programme.
What You'll Learn
- Clinical Trial Design: Phase I–III, adaptive designs, sample size, randomisation
- Survival Analysis: Kaplan-Meier, Cox regression, time-to-event modelling
- Longitudinal Data Analysis: Mixed models, GEE, repeated measures ANOVA
- Genetic Epidemiology: GWAS, polygenic scores, family-based designs
- Epidemiological Methods: Cohort, case-control, matched study design
- Regulatory Statistics: ICH E9(R1), estimands, FDA/EMA submission standards
- Bayesian Clinical Methods: Prior elicitation, adaptive Bayesian trials, decision rules
- Biostatistical Computing: Advanced R (survival, nlme, lme4), SAS PROC MIXED/LIFETEST
Clinical Trial Design
Randomised controlled trials, Phase I dose-finding, adaptive trial methods, sample size calculation, and CONSORT reporting.
Genetic Epidemiology
Genome-wide association studies, polygenic risk scores, linkage disequilibrium, Mendelian randomisation, and population stratification.
Survival Analysis
Non-parametric (Kaplan-Meier), semi-parametric (Cox PH), parametric survival models, and competing risks analysis.
Longitudinal Data Analysis
Linear and generalised mixed-effects models, GEE, missing data (MCAR/MAR/MNAR), multiple imputation strategies.
Pharmaceutical Statistics
Bioequivalence testing, pharmacokinetic/pharmacodynamic modelling, dose-response, and non-inferiority trial analysis.
Epidemiological Methods
Cohort and case-control design, relative risk, odds ratios, confounding adjustment, and propensity score analysis.
Biostatistical Computing
Advanced R programming (survival, nlme, lme4, mice) and SAS procedures (PROC MIXED, LIFETEST, PHREG) for healthcare data.
Regulatory Statistics
ICH E9(R1) estimand framework, FDA and EMA guidance, statistical analysis plans, and regulatory submission preparation.
Click any course to view its objective and learning outcomes.
BST 501 Statistical Inference +
Objective
To establish frequentist and likelihood-based inference for biological data.
Learning Outcomes
- Apply MLE and likelihood ratio tests.
- Construct confidence intervals.
- Apply asymptotic theory.
- Use sufficient statistics.
- Discuss frequentist vs Bayesian approaches.
BST 502 Linear Models & Regression +
Objective
To fit and diagnose linear regression models for biomedical data.
Learning Outcomes
- Apply OLS to biomedical datasets.
- Diagnose violations.
- Apply model selection.
- Use interaction terms.
- Communicate regression results.
BST 503 Generalised Linear Models +
Objective
To extend regression to non-normal outcomes.
Learning Outcomes
- Apply logistic regression to binary outcomes.
- Use Poisson regression for count data.
- Apply gamma regression for skewed continuous data.
- Diagnose GLM assumptions.
- Apply zero-inflated models.
BST 504 Survival Analysis +
Objective
To model time-to-event data including censored observations.
Learning Outcomes
- Estimate survival functions via Kaplan-Meier.
- Apply Cox proportional hazards.
- Use Weibull and exponential parametric models.
- Apply competing-risks analysis.
- Address censoring and truncation.
BST 505 Clinical Trials Design +
Objective
To design and analyse clinical trials rigorously.
Learning Outcomes
- Compute sample sizes for trials.
- Design Phase I-III trials.
- Apply group sequential designs.
- Address missing data via multiple imputation.
- Discuss intention-to-treat analysis.
BST 506 Longitudinal & Multilevel Models +
Objective
To model repeated measurements and clustered data.
Learning Outcomes
- Apply mixed-effects models.
- Use generalised estimating equations.
- Apply growth curve models.
- Address dropout in longitudinal studies.
- Use multilevel models for hierarchical data.
BST 507 Epidemiological Methods +
Objective
To apply statistical methods to epidemiological research.
Learning Outcomes
- Apply case-control and cohort designs.
- Compute odds ratios and relative risks.
- Use stratification and confounding adjustment.
- Apply directed acyclic graphs.
- Conduct meta-analysis.
BST 508 Bayesian Methods in Health +
Objective
To apply Bayesian inference in biomedical research.
Learning Outcomes
- Apply Bayes' rule with conjugate priors.
- Use MCMC for posterior simulation.
- Apply Bayesian clinical trial designs.
- Use hierarchical Bayesian models.
- Communicate Bayesian results to clinicians.
BST 509 Statistical Genetics +
Objective
To apply statistical methods to genetic and genomic data.
Learning Outcomes
- Apply GWAS analysis.
- Use linkage disequilibrium.
- Apply heritability estimation.
- Address population stratification.
- Apply Mendelian randomisation.
BST 510 Causal Inference +
Objective
To estimate causal effects from observational and experimental data.
Learning Outcomes
- Apply potential-outcomes framework.
- Use propensity score methods.
- Apply instrumental variables.
- Use regression discontinuity.
- Discuss confounding and effect modification.
BST 511 Statistical Programming (R/SAS) +
Objective
To use industry software for biostatistical analysis.
Learning Outcomes
- Implement methods in R and SAS.
- Build reproducible analysis pipelines.
- Use Bioconductor for genomics.
- Apply CDISC standards.
- Validate code against reference implementations.
BST 512 Master's Project +
Objective
To complete an original biostatistics research project.
Learning Outcomes
- Frame a biomedical research question.
- Apply rigorous statistical methodology.
- Use real biomedical data.
- Write a research-quality dissertation.
- Present to clinicians and statisticians.
Biostatistician
Lead statistical design and analysis for clinical studies at pharmaceutical companies, CROs, and academic medical centres.
Clinical Research Statistician
Develop SAPs, analyse trial data, and prepare regulatory-quality statistical reports for drug approval submissions.
Epidemiologist
Investigate disease patterns and risk factors using population-level data in public health agencies and universities.
Pharmaceutical Data Analyst
Analyse preclinical and clinical datasets for major pharmaceutical companies supporting drug development decisions.
Health Data Scientist
Apply ML and advanced statistical methods to NHS, insurance, or national health datasets to improve patient outcomes.
Regulatory Affairs Statistician
Prepare and defend statistical methodology in regulatory submissions to the FDA, EMA, or MHRA.
Why D'Math University
WHO & NHS Partnerships
Direct links with WHO Geneva and NHS England provide access to real public health datasets and policy-relevant research opportunities.
Regulatory Alignment
Curriculum mirrors ICH, FDA, and EMA statistical standards — graduates can contribute to regulatory submissions from Day 1.
Dual Software Mastery
Graduate proficient in both R and SAS — the two dominant platforms in clinical trials and pharmaceutical statistics worldwide.
Pharma Industry Links
Dissertation placements and internship opportunities with leading pharmaceutical and CRO partners in Europe and North America.
January and September intakes — healthcare sector sponsorship available