Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review



Stevens, David, Lane, Deirdre A ORCID: 0000-0002-5604-9378, Harrison, Stephanie L ORCID: 0000-0002-8846-0946, Lip, Gregory YH and Kolamunnage-Dona, Ruwanthi ORCID: 0000-0003-3886-6208
(2021) Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review. BMC MEDICAL RESEARCH METHODOLOGY, 21 (1). 283-.

Access the full-text of this item by clicking on the Open Access link.

Abstract

<h4>Objective</h4>The identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories.<h4>Methods</h4>We screened MEDLINE-Ovid from inception until 3 June 2020. MeSH and text search terms covered three areas: data type, modelling type and disease area including search terms such as "longitudinal", "trajector*" and "cardiovasc*" respectively. Studies were filtered to meet the following inclusion criteria: longitudinal individual patient data in adult patients with ≥3 time-points and a CVD or mortality outcome. Studies were screened and analyzed by one author. Any queries were discussed with the other authors. Comparisons were made between the methods identified looking at assumptions, flexibility and software availability.<h4>Results</h4>From the initial 2601 studies returned by the searches 80 studies were included. Four statistical approaches were identified for modelling the longitudinal data: 3 (4%) studies compared time points with simple statistical tests, 40 (50%) used single-stage approaches, such as including single time points or summary measures in survival models, 29 (36%) used two-stage approaches including an estimated longitudinal parameter in survival models, and 8 (10%) used joint models which modelled the longitudinal and survival data together. The proportion of CVD risk prediction models created using longitudinal data using two-stage and joint models increased over time.<h4>Conclusions</h4>Single stage models are still heavily utilized by many CVD risk prediction studies for modelling longitudinal data. Future studies should fully utilize available longitudinal data when analyzing CVD risk by employing two-stage and joint approaches which can often better utilize the available data.

Item Type: Article
Uncontrolled Keywords: Cardiovascular disease, Longitudinal, Repeated measures, Risk prediction, Methodological review
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Faculty of Health and Life Sciences > Institute of Population Health
Depositing User: Symplectic Admin
Date Deposited: 18 Jan 2022 11:22
Last Modified: 18 Jan 2023 21:15
DOI: 10.1186/s12874-021-01472-x
Open Access URL: https://doi.org/10.1186/s12874-021-01472-x
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3147101