Peter Kirwan

About

I’m a Research Associate at the MRC Biostatistics Unit in Cambridge. My PhD focussed on applications of multi-state models to estimate infectious disease burden, specifically HIV and COVID-19. My current research involves the development of multi-state “back-calculation” models to estimate HIV incidence and undiagnosed HIV prevalence.

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Statistical methods for survival data

This series of blog posts provides an introduction to statistical methods for survival data, with applications for infectious disease modelling and epidemiology. Topics include: classical survival methods, competing risks analysis, and causal inference from observational data.

Part I: Key concepts in infectious disease research

An overview of key concepts, introducing some of the challenges in estimating the burden of infectious disease.

Part II: Survival analysis

18 min

Censoring

Truncation

An introduction to survival analysis, time-to-event data, and how we handle censored observations in epidemiological research.

Part III: Classical survival methods

6 min

Likelihood

Survival models

Exploring two fundamental approaches to analysing survival data: non-parametric Kaplan-Meier estimation and semi-parametric Cox proportional hazards models.

Part IV: Competing risks survival methods

6 min

Competing risks

Stratification

How to handle scenarios where multiple possible events can occur, and the challenges this presents for traditional survival methods.

Part V: Multi-state models

14 min

Transition intensity

Markov property

Some theory of multi-state models and their applications in epidemiology, including estimating transition intensities and the time spent in a state.

Part VI: Statistical inference for multi-state models

10 min

Multi-state models

Statistical inference

Details of two statistical inference methods for multi-state models and how these are well-suited to the investigation of intermittently-observed data.