Upcoming GRBIO seminars
Organizer: Cristian Tebé
Online, si voleu assistir-hi, contacteu amb grbio@grbio.eu
Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
Distributed lag non-linear models (DLNMs) are the standard approach for modelling lagged non-linear associations but are limited in small-area analyses. Recent spatial Bayesian DLNMs (SB-DLNMs) address these limitations by jointly modelling spatial dependence and non-linear lagged effects in a one-stage framework. This approach enables robust estimation of geographically varying risks in small areas by borrowing strength across space. The presentation introduces SB-DLNMs, demonstrates their application in epidemiological studies, and discusses key gaps and future methodological directions.
Biosketch
Marcos Quijal Zamorano is a postdoctoral researcher at the Climate Epidemiology & Public Health research group at University of Bern, supervised by Dr. Ana Maria Vicedo Cabrera. His research focuses on methodological developments in environmental epidemiology, extending spatial and flexible exposure–response models for small-area analyses, developed during his PhD at ISGlobal. He participated in studies that assessed temperature-related health impacts across Europe under current and future climate conditions and informed heat–health early warning systems.
Online, si voleu assistir-hi, contacteu amb grbio@grbio.eu
Evidence in small samples? Lessons learned in study design of prognostic indices in HNSCC tumours
In clinical oncology, prognostic markers and indices available at diagnosis offer timely input for clinicians to decide an appropriate treatment pathway. Their risk stratifications provide helpful points of comparison in an internal review of health outcomes within a cohort of patients. In this talk, we’ll discuss how we can garner literature-relevant evidence in a small cohort through study design decisions, and how it is conditioned by the index construction.
Biosketch
Gonzalo Peón Pena is a practising statistician at the Infections and Cancer Unit The Cancer Epidemiology Research Programme (PREC) at the Catalan Institute of Oncology (ICO). His main line of work centers on prognostic and diagnostic performances markers and analysing survival outcomes in head and neck cancer tumours.
Online, si voleu assistir-hi, contacteu amb grbio@grbio.eu
Estimands and Estimation Strategies for Platform Trials with Time Trends
"In platform trials, model-based approaches have been proposed to mitigate bias from time trends. However, these methods typically condition on time, yielding treatment effect estimates specific to particular calendar periods. While such conditional estimates are unbiased for their respective time-specific estimands, investigators typically seek an unconditional treatment effect - one that does not depend on calendar time and reflects the effect in the overall trial population. This raises fundamental questions: when combining data from multiple periods, what is the appropriate target estimand? What defines the overall trial population if arms continuously enter and leave? Which estimator should be used?
In this work, we examine both conditional and marginal estimands in platform trials with time trends, clarifying the target populations of inferential interest. We evaluate model-based approaches, G-computation and augmented inverse probability weighting estimators, comparing their properties, including bias and variance. We discuss how the choice of estimand and the target population, as well as which trial data to use for estimation, affects the performance of estimators."
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