Welcome!

Marta Bofill Roig

Postdoctoral researcher

Medical University of Vienna

I am a postdoctoral researcher at the Center for Medical Statistics, Informatics and Intelligent Systems. My research is focused on methods and software for clinical trials designs involving multiple endpoints.

I graduated in Mathematics at the University of Barcelona (UB) in 2014. During my bachelor degree studies, I focused my interests on topics related to Statistics. I obtained my Master’s degree in Statistics and Operations Research (MESIO UPC-UB), a joint programme by the University of Barcelona (UB) and the Universitat Politècnica de Catalunya-BarcelonaTECH (UPC), in 2016. I obtained my PhD at the Department of Statistics and Operations Research of the UPC under the supervision of Prof. Guadalupe Gómez Melis.

Interests

Clinical Trials
Adaptive designs
Survival Analysis

Education

PhD in Statistics and Operations Research, 2020

Universitat Politècnica de Catalunya
MSc in Statistics and Operations Research, 2016

Universitat Politècnica de Catalunya and Universitat de Barcelona
BSc in Mathematics, 2014

Universitat de Barcelona

Recent Publications

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Marta Bofill Roig, Guadalupe Gómez Melis, Martin Posch, Franz Koenig (2022). Adaptive clinical trial designs with selection of composite endpoints and sample size reassessment.

Stefan M Gold, Marta Bofill Roig, J Jaime Miranda, Carmine Pariante, Martin Posch, Christian Otte (2022). Platform trials and the future of evaluating therapeutic behavioural interventions. In Nature Reviews Psychology.

PDF

Marta Bofill Roig, Pavla Krotka, Carl-Fredrik Burman, Ekkehard Glimm, Katharina Hees, Peter Jacko, Franz Koenig, Dominic Magirr, Peter Mesenbrink, Kert Viele, Martin Posch (2021). On model-based time trend adjustments in platform trials with non-concurrent controls.

Preprint Code

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Software

R packages & Shiny apps

R packages:

CompARE: Sample size and effect size calculation for composite endpoints. [GitHub] [CRAN] [website] [Shiny-app]
Survbin: Two-sample statistics for binary and time-to-event outcomes. [GitHub] [website]
survmixer: Design of clinical trials with survival endpoints based on binary response. [GitHub] [CRAN] [website]

Repositories:

Non-concurrent controls: Model-based time trend adjustments in platform trials with non-concurrent controls. [GitHub]
eselect: R package for adaptive trial designs with endpoint selection and sample size reassessment. [GitHub]

Fun stuff:

pum! (together with Pol Castellano and Guillermo Villacampa): Shiny app to explore Florence Nightingale’s data in Crimean War. [GitHub] [Shiny-app] [YouTube-Tutorial]