Dr Alberto Alvarez-Iglesias

Clinical Research Biostatistician

Research interests

  • Alternative non-parametric methods for the analysis of survival data.
  • Design and analysis of clinical trials.
  • Tree based methods.
   

Research overview

Within the Biostatistics Unit in the HRB Clinical Research Facility, NUI Galway we provide statistical considerations for oncology trial protocols (design, data collection, sample size calculations etc), write statistical analysis plans, process and analyse data for interim analysis and Independent Data Monitoring Committees meetings, produce final statistical reports, and advise on presentation and interpretation of results for publications.

Some of the activities we have been involved in the last year include the writing of 2 Statistical Analysis Plans for the all Ireland Cooperative Clinical Research Group ICORG, the statistical analysis for an oral presentation in the American Society for Radiation Oncology conference ASTRO of a randomised non-inferiority phase III trial, the writing of statistics reports for IDMC meetings and the statistical analysis and production of the final technical report of a phase II single arm trial.

Other activities include the creation of an online calculator for early inefficacy stops in superiority trials with survival outcomes. This calculator is based on a general approach proposed by Feidlin et al (2010) when there is a need for inefficacy monitoring rules when modelling time to event data (see reference within the link https://hrbcrfg.shinyapps.io/interimSur/). In general, multi-stage or group sequential methods are common in many branches of scientific research. The aim of these methods is to pre-specify, prior to the start of data collection, the timing and manner in which a sequence of interim analyses will be conducted within the study. For instance, in a Randomised Clinical Trial, one might want to investigate as soon as possible whether patients under a new treatment are being exposed to a harmful pharmaceutical drug, in which case the study should be stopped for futility. On the contrary, if the new treatment is leading to positive results, early stopping means that patients will benefit earlier from the new treatment. Unlike other group sequential methods, the approach proposed by Feidlin et al (2010) has the advantage that the interim looks can be defined post design, meaning that there is no need for sample size modifications, even after some of the data have already been collected. The online calculator allows the user to simulate data using different survival distributions, to evaluate how the the incorporation of these rules post design will affect the significance level and the power of the test.

Selected publications

  • Alvarez-Iglesias, A., Hinde, J., Scarrott, C.J. and Newell, J. (2015). Summarising censored survival data using the mean residual life function. Statistics in Medicine; Accepted 23/12/14.
  • Glynn, L.G., Hayes, P.S., Casey, M., Glynn, F., Alvarez-Iglesias, A., Newell, J., O'Laighin, G., Heaney, D., O'Donnell, M. and Murphy, A.W. (2014). Eff ectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trial. British Journal of General Practice; 64(624); 384-391.
  • Hurley, L., Kelly, L., Garrow, A.P., Glynn, L.G., McIntosh, C., Alvarez-Iglesias, A., Avalos, G. and Dinneen, S.F (2013). A prospective study of risk factors for foot ulceration: The West of Ireland Diabetes Foot Study. QJM; 106(12); 1103-1110.
  • Glynn, L. G., Hayes, P. S., Casey, M., Glynn, F., Alvarez-Iglesias, A., Newell, J., Laighin, G., et al. (2013). SMART MOVE-a smartphone-based intervention to promote physical activity in primary care: study protocol for a randomized controlled trial. Trials; 14(1); 157.
  • Alvarez-Iglesias, A., Newell, J., Hinde, J. and Glynn, L. (2010). Robust Survival Trees Based on Node Re-sampling. In Proceedings of the 25th International Workshop on Statistical Modelling, Glasgow.