Analysing trajectories of a longitudinal exposure: A causal perspective on common methods in lifecourse research


Autoři: Sarah C. Gadd aff001;  Peter W. G. Tennant aff001;  Alison J. Heppenstall aff001;  Jan R. Boehnke aff005;  Mark S. Gilthorpe aff001
Působiště autorů: Leeds Institute of Data Analytics, University of Leeds, Leeds, England, United Kingdom aff001;  School of Geography, University of Leeds, Leeds, England, United Kingdom aff002;  School of Medicine, University of Leeds, Leeds, England, United Kingdom aff003;  The Alan Turing Institute, London, England, United Kingdom aff004;  School of Nursing and Health Sciences, University of Dundee, Dundee, Scotland, United Kingdom aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0225217

Souhrn

Longitudinal data is commonly analysed to inform prevention policies for diseases that may develop throughout life. Commonly methods interpret the longitudinal data as a series of discrete measurements or as continuous patterns. Some of the latter methods condition on the outcome, aiming to capture ‘average’ patterns within outcome groups, while others capture individual-level pattern features before relating these to the outcome. Conditioning on the outcome may prevent meaningful interpretation. Repeated measurements of a longitudinal exposure (weight) and later outcome (glycated haemoglobin levels) were simulated to match three scenarios: one with no causal relationship between growth rate and glycated haemoglobin; two with a positive causal effect of growth rate on glycated haemoglobin. Two methods that condition on the outcome and one that did not were applied to the data in 1000 simulations. The interpretation of the two-step method matched the simulation in all causal scenarios, but that of the methods conditioning on the outcome did not. Methods that condition on the outcome do not accurately represent a causal relationship between a longitudinal pattern and outcome. Researchers considering longitudinal data should carefully determine if they wish to analyse longitudinal data as a series of discrete time points or by extracting pattern features.

Klíčová slova:

Birth weight – Covariance – Diabetes diagnosis and management – Directed acyclic graphs – Science policy – Simulation and modeling


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Článok vyšiel v časopise

PLOS One


2019 Číslo 12