The Florence Center for Data Science and the Department of Statistics, Computer Science, Application “G. Parenti” DiSIA are happy to invite you to the joint event: a new Special Guest Seminar!

The Seminar will be held on-site and online Thursday 11th May 2023 from 4.30 – 5.30 PM

Our guest will be Björn Bornkamp – Statistical Methodologist at Novartis.

The seminar will be held in Aula 205 (ex 32) – Viale Morgagni 59.

The Seminar will be available also online.

Please register here to participate online: https://us02web.zoom.us/webinar/register/WN_RgnfqAVYSJaA5JYRePVNUw

Title: Estimand and analysis strategies for recurrent event endpoints in the presence of a terminal event

Abstract:

Recurrent event endpoints are commonly used in clinical drug development. One example is the number of recurrent heart failure hospitalizations, which is used in the context of clinical trials in the chronic heart failure (CHF) indication. A challenge in this context is that patients with CHF are at an increased risk of dying. For patients who died, further heart failure hospitalizations can no longer be observed. As a treatment may affect both mortality and the number of hospitalizations, a naive comparison of the number of hospitalizations across treatment arms can be misleading even in a randomized clinical trial. An investigational treatment may, for example, reduce mortality compared to a control, but this may lead to more observed hospitalizations if severely ill patients with high risk of repeated hospitalizations die earlier under the control treatment. In this talk we will review this issue and different estimand and analysis strategies. We will then describe a Bayesian modelling strategy to target a principal stratum estimand in detail. The model relies on joint modelling of the recurrent event and death processes with a frailty term accounting for within-subject correlation. The analysis is illustrated in the context of a recent randomized clinical trial in the CHF indication.