Introduction
What is new?
Key findings- •
Although latent class growth analysis (LCGA) and latent class growth mixture modeling (LCGMM) seem to be preferable above the more simple methods, all classification methods should be applied with great caution.
What this adds to what was known?- •
To our knowledge, this is the first study that compares five statistical methods to classify developmental trajectories over time with each other in a practical way without any complicated mathematical issues.
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When there are only linear developments over time, LCGA performed the best.
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When there are both linear and quadratic developments over time, all classification methods did not perform well.
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The number of classes in the optimal solution derived from LCA and LCGA is (much) bigger compared with LCGMM.
What is the implication and what should change now?- •
This article can be used by practical researchers to choose the optimal method to classify developmental trajectories over time.
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This article shows that all classification methods should be applied with great caution.
Within medical and epidemiological research, prospective cohort studies become more and more important. One of the main reasons for this increasing popularity is the possibility to study individual development over time. In addition, researchers are often interested in dividing the cohort under study into groups of subjects with comparable developmental trajectories. First, as a tool to describe the population under study and second as a first step to study either the determinants of different trajectories or the consequences of different trajectories. Although the division into subgroups can be done in many different ways, it is surprising that in medical and epidemiological research, sophisticated methods are hardly used. This could be owing to the fact that most of them are based on structural equation modeling (SEM) [1], a fairly complex statistical technique particularly popular in psychology and social science, but not so much in medical science and epidemiology. Reviewing the available literature, the techniques used to define subgroups of developmental trajectories can be divided into (1) cross-sectional (naive) techniques (i.e., techniques that ignore the longitudinal structure of the data) and (2) longitudinal techniques that define the subgroups according to the parameters of the individual growth curves.
The few examples of the classification of developmental trajectories found in the medical and epidemiological literature deal mainly with the development of substance abuse [2], [3], [4], [5]; the development of functional limitations in the elderly [6], [7], [8]; pediatrics [9], [10], [11], [12]; and some specific topics, such as low back pain [13], night time bladder control [14], anxiety and depressive disorders [15], and body fatness [16]. It is striking to see that in these medical and epidemiological studies, there is no consistency in the use of a statistical approach to classify developmental trajectories. The methodology differs from relatively simple cross-sectional methods to complicated SEM techniques.
The purpose of the present study is to compare several methods with each other, which classifies individuals according to their developmental trajectories. This will first be done on two data sets in which particular developments are manipulated into the data and second on a real-life data set.