Joint or simultaneous modelling of related longitudinal and time-to-event data for a network of treatments across multiple data sources

Sudell, Maria ORCID: 0000-0002-7919-4981 (2025) Joint or simultaneous modelling of related longitudinal and time-to-event data for a network of treatments across multiple data sources. [Poster]

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Description

Longitudinal data is recorded repeatedly over time, allowing trends over time to examined as well as results at a particular timepoint (examples include monthly blood pressure measurements, repeated laboratory measurements or regular mental health assessments). Time-to-event or survival data records the time until an individual experiences a clinical event of interest or withdraws from the dataset for unrelated reasons (for example time until first stroke, or time until hospital discharge). Commonly in healthcare data, related longitudinal and time-to-event data exists (for example, blood pressure measured repeatedly over time might be related in some way or predictive of time to first stroke). Ignoring this relationship could lead to misleading or biased results in analyses, so joint models that simultaneously evaluate the longitudinal and time-to-event outcomes and their relationship are useful. In healthcare, many treatments may exist for a particular condition, for a given group of patients (a population). To make properly informed decisions about how these treatments compare to each other, we need to be able to compare them simultaneously. This can be difficult, if different data sources only involve a subset of the possible treatments (for example, if many clinical trials have been conducted for a given condition, but they each examine subsets of the possible treatments). Network Meta Analysis (NMA) provides an approach to pool data from multiple trials, each of which compare a subset of treatments from the complete set of possible treatments for a population (as long as a connected “network” of treatments can be drawn from the available data). Approaches for separate longitudinal NMA and separate time-to-event NMA currently exist, but not for NMA involving both longitudinal and time-to-event data. This research provides novel methodology and code linking joint modelling with NMA approaches, to better allow evaluation of all available treatment options and their effects on longitudinal and time-to-event outcomes of interest. The proposed methodology and code is applied to a multi-study cardiovascular dataset, containing repeated blood pressure measurements, and the times until various cardiovascular events (stroke, myocardial infarction, death), for a network of anti-hypertensive treatments.

Item Type:	Poster
Uncontrolled Keywords:	joint modelling, longitudinal, Time-to-event
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health
Depositing User:	Symplectic Admin
Date Deposited:	01 Apr 2025 07:59
Last Modified:	01 Apr 2025 07:59
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3191144