Michael Barrowman is a Data Scientist working for Mirador Analytics providing disclosure risk analysis and Expert Determinations to ensure client data is viable under HIPAA regulations, and maintains an internal R package for the quantification of risk.

He is currently finalising his Thesis on Multi-State Clinical Prediction Models in Renal Replacement Therapy as a PhD Candidate within the University of Manchester. His PhD project encompasses the development and validation of a multi-state clinical predication model, as well as the methodological advancements to produce such a model. This has led to multiple publications and the creation of software as a by-product.

He has previously worked within both the public and private sector providing data analysis to many industries, particularly education and health. During this time, he has contributed to SAPs and SOPs for a pioneering pragmatic clinical trial and improved the efficiency of examination marking by over 10%.

He is interested in Data Science, particularly using R and RStudio to their fullest potential, encouraging others to do the same and is an advocate for neat and reproducible coding practices. He also enjoys learning new programming languages and has become adept at C++, JavaScript and Java.

He lives in Merseyside, UK with his two children and he enjoy walks down by the local canal, through nearby forested areas and trips to the park as often as possible as his daughter’s favourite outdoor activity is “going on adventures”.


  • HIPAA Privacy Rules
  • Data Science
  • Clinical Prediction Modelling
  • Multi-State Models


  • PhD in Medicine (pending), 2021

    University of Manchester

  • MSci in Mathematics, 2013

    University of Lancaster







Data Visualisation








Data Scientist


Jan 2021 – Present Melrose, Scottish Borders, UK
Focusing on health data compliance to ensure the privacy of individuals within the larger healthcare scope. Reporting on data deidentification with expert determinations of disclosure risk

PhD Candidate

University of Manchester

Oct 2016 – Present Manchester, UK
The goal of this PhD is to improve the academic knowledge surrounding Multi-State Clinical Prediction Models (MSCPMs). To accomplish this, I am writing articles to solve methodological issues that are yet to be addressed and applying these novel techniques (along with the present literature) to develop and validate an MSCPM to predict outcomes for Chronic Kidney Disease patients.
(2020). Toward a Framework for the Design, Implementation, and Reporting of Methodology Scoping Reviews. J CLIN EPIDEMIOL.


(2019). How Unmeasured Confounding in a Competing Risks Setting Can Affect Treatment Effect Estimates in Observational Studies. BMC Med Res Methodol.


(2018). Study Investigating the Generalisability of a COPD Trial Based in Primary Care (Salford Lung Study) and the Presence of a Hawthorne Effect. BMJ Open Respir Res.