Grup de Recerca en Bioestadística i Bioinformàtica

Upcoming GRBIO seminars

Organizer: Cristian Tebé

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."