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

BMC Medical Research Methodology

Ruwanthi Kolamunnage-Dona¹  David Stevens²  Stephanie L. Harrison²  Deirdre A. Lane³  Gregory Y. H. Lip³ 

[1] Department of Health Data Science, Institute of Population Health, University of Liverpool, Liverpool, UK;Liverpool Centre for Cardiovascular Science, University of Liverpool, L7 8TX, Liverpool, UK;Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK;Liverpool Centre for Cardiovascular Science, University of Liverpool, L7 8TX, Liverpool, UK;Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK;Department of Clinical Medicine, Aalborg University, Aalborg, Denmark;

关键词: Cardiovascular disease;    Longitudinal;    Repeated measures;    Risk prediction;    Methodological review;   

DOI  :  10.1186/s12874-021-01472-x

来源: Springer

【 摘 要 】

ObjectiveThe identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories.MethodsWe 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.ResultsFrom 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.ConclusionsSingle 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.

【 授权许可】

CC BY