Researcher profile

Alberto Alvarez-Iglesias

Extensive background in Statistics and Mathematics with a convincing track record of collaborative and primary research in Biostatistics as well as considerable experience in academic related duties. Main research interest is in the development of statistical techniques that enable the translation of complicated analytical findings to a non-technical audience, e.g. patients, policy makers etc. Examples of my outputs in this space are the development of an alternative pruning approach for tree based methods (often used by clinicians to model responses in an easily interpretable manner) and the use of the mean residual life function (which summarises the survival experience of a patient in units of time rather than in probabilities or hazards) as a summary of censored survival data, with a novel non-parametric estimator for low or moderate type I censoring. Currently I am working in a derivation of a score for the risk of ischemic stroke using an incidence prediction model based on modifiable and non-modifiable risk factors as measured in the INTERSTROKE case-control study. Overall leadership role in the Biostatistics Unit in the Clinical Research Facility Galway including protocol development (e.g. design, data collection, sample size calculations), writing statistical analysis plans, processing and analysing data for interim analyses and Independent Data Monitoring Committees meetings, producing final statistical reports and advising on presentation and interpretation of results for publications. Leading role organising and conducting meetings with the Biostatistics and Data Management research teams to clarify objectives, develop team work plans/timetables for research and support staff and to communicate progress. Strong statistical programming skills in R and a vast knowledge of some of its translational tools such as shiny. Using these tools, I have created an online calculator for incorporating early inefficacy stopping rules in superiority clinical trials with survival outcomes. I have also co-authored a package in R, DynNom, which demonstrates the results of linear, generalised linear and proportional hazard models as a dynamic nomogram in a web browser.