title: Joint modelling compared with two stage methods for analysing longitudinal data and prospective outcomes: A simulation study of childhood growth and BP.
abstract: There is a growing debate with regards to the appropriate methods of analysis of growth trajectories and their association with prospective dependent outcomes. Using the example of childhood growth and adult BP, we conducted an extensive simulation study to explore four two-stage and two joint modelling methods, and compared their bias and coverage in estimation of the (unconditional) association between birth length and later BP, and the association between growth rate and later BP (conditional on birth length). We show that the two-stage method of using multilevel models to estimate growth parameters and relating these to outcome gives unbiased estimates of the conditional associations between growth and outcome. Using simulations, we demonstrate that the simple methods resulted in bias in the presence of measurement error, as did the two-stage multilevel method when looking at the total (unconditional) association of birth length with outcome. The two joint modelling methods gave unbiased results, but using the re-inflated residuals led to undercoverage of the confidence intervals. We conclude that either joint modelling or the simpler two-stage multilevel approach can be used to estimate conditional associations between growth and later outcomes, but that only joint modelling is unbiased with nominal coverage for unconditional associations.
authors.affiliation: 1 School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors.forename: A
authors.initials: A
authors.lastname: Sayers
authors.affiliation: 1 School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors.forename: J
authors.initials: J
authors.lastname: Heron
authors.affiliation: 1 School of Social and Community Medicine, University of Bristol, Bristol, UK.|2 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
authors.forename: Adac
authors.initials: A
authors.lastname: Smith
authors.affiliation: 1 School of Social and Community Medicine, University of Bristol, Bristol, UK.|2 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
authors.forename: C
authors.initials: C
authors.lastname: Macdonald-Wallis
authors.affiliation: 3 Division of Epidemiology and Biostatistics, School of Medicine, University of Leeds, Leeds, UK.
authors.forename: M S
authors.initials: MS
authors.lastname: Gilthorpe
authors.affiliation: 4 Department of Statistics, London School of Economics, London, UK.
authors.forename: F
authors.initials: F
authors.lastname: Steele
authors.affiliation: 1 School of Social and Community Medicine, University of Bristol, Bristol, UK.|2 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
authors.forename: K
authors.initials: K
authors.lastname: Tilling
mesh_terms: D000328:Adult; D015982:Bias; D001794:Blood Pressure; D049628:Body Size; D002648:Child; D002657:Child Development; D002675:Child, Preschool; D016001:Confidence Intervals; D006128:Growth; D006801:Humans; D007231:Infant, Newborn; D008137:Longitudinal Studies; D011446:Prospective Studies
keywords: growth; joint model; lifecourse epidemiology; measurement error; multilevel model