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Joint modelling compared with two stage methods for analysing longitudinal data and prospective outcomes: a simulation study of childhood growth and BP

Sayers, A., Heron, J., Smith, A., Macdonald-Wallis, C., Gilthorpe, M., Steele, F. and Tilling, K. (2017) Joint modelling compared with two stage methods for analysing longitudinal data and prospective outcomes: a simulation study of childhood growth and BP. Statistical Methods in Medical Research, 26 (1). pp. 437-452. ISSN 0962-2802

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Identification Number: 10.1177/0962280214548822

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.

Item Type: Article
Official URL: http://smm.sagepub.com/
Additional Information: © 2014 The Authors © CC BY 3.0
Divisions: Statistics
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 09 Jun 2015 08:35
Last Modified: 20 Jun 2019 02:27
Projects: G1000726, MR/J011932, RES-576-25-0032, MC_UU_12013/5
Funders: Medical Research Council, Economic and Social Research Council, UK Medical Research Council, University of Bristol
URI: http://eprints.lse.ac.uk/id/eprint/62246

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