Application of Latent Curve Models in Medical Research – A Review
Application of Latent Curve Models in Medical Research – A Review
European Neurological Review, 2009;4(2):52-6
Abstract
Recently, latent curve modelling (LCM) has received increasing attention in the analysis of longitudinal data. It is a method to model individual change and to assess the effects of co-variates and the relationship among multiple outcomes. It provides an integrated and flexible approach in modelling developmental processes from both inter- and intra-individual perspectives. Similar to conventional longitudinal analysis, the main objectives of this model are to characterise changes in the response of interest over time and to examine the selected covariates that contribute to those changes. In this article the fundamental principle of LCM is briefly introduced. Several important kinds of LCM, including linear LCM, non-linear LCM, multilevel LCM and mixture LCM, together with their applications in medical research, are reviewed. We believe that this statistical technique should become more popular in medical applications, and that the medical field would benefit from increased use of this powerful and flexible statistical method.
Keywords
Longitudinal data, medical application, latent curve models
Disclosure: The research described herein was fully supported by a research grant (GRF 450607) from the Research Grants Council of the Hong Kong Special Administration Region, a grant (grant no. 931012) from the Health and Health Services Research Fund in Hong Kong and a research grant (grant no. 16000-3126133) from the Hundred Talent Program of Sun Yat-sen University. The authors have no conflicts of interest to declare.
Received: 21 January 2009 Accepted: 7 October 2009
Correspondence: Timothy Kwok, Professor, Department of Medicine and Therapeutics, Prince of Wales Hospital, the Chinese University of Hong Kong, Shatin,New Territories, Hong Kong. E: tkwok@cuhk.edu.hk
Longitudinal data comprising repeated measurements of the same individuals on a number of occasions arise frequently in a wide range of fields: medicine, public health, psychology, biology and more. The main objectives of a longitudinal study are to characterise changes in the response of interest over time and to examine the selected co-variates that contribute to those changes. Traditional methods used to analyse longitudinal data are varied. Examples of these methods include autoregressive models, repeated measures multivariate analysis of variance, mixed-effects models, multiple regressions and so on.
Recently, there has been growing interest in models that have the ability to incorporate information concerning not only the group or population, but also changes in the individual. Latent (growth) curve modelling allows for the testing of complex models regarding developmental trends at both inter- and intra-individual levels. It has received increasing attention in medical research recently and has been well recognised as a useful longitudinal technique in the analysis of patterns of change.1–6
Latent Curve Model
The latent curve model (LCM) is a method to model individual change, assess the effects of co-variates and assess the relationshipamong multiple outcomes, and take the measurement error into account. It provides a means of modelling developmental processes from both the inter- and intra-individual perspective. Generally speaking, LCM consists of two stages in modelling the patterns of change. In the first stage, the repeated measures of each individual across time are fitted through a regression-type curve, which is either linear or non-linear. In the second stage, the focus of the analysis is on the latent growth factors, which are used to identify the individual’s growth curve. The interest is no longer specifically on the original repeated measures observed, but on the unobserved latent growth factors that lead to the repeated measures. The LCM not only takes into account the mean of latent growth factors, which represent the group-level change, but also considers the variances that measure the degree of individual differences. This combination of group- and individual-level analyses is synthesised in the LCM procedure.
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Longitudinal data, medical application, latent curve models, latent factor model, latent growth curve modelling, latent class model, latent trial model,
