Asset Details
Do you wish to reserve the book?
Classifying developmental trajectories over time should be done with great caution: a comparison between methods
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Classifying developmental trajectories over time should be done with great caution: a comparison between methods
Do you wish to request the book?
Classifying developmental trajectories over time should be done with great caution: a comparison between methods
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Classifying developmental trajectories over time should be done with great caution: a comparison between methods
2012
Overview
In the analysis of data from longitudinal cohort studies, there is a growing interest in the analysis of developmental trajectories in subpopulations of the cohort under study. There are different advanced statistical methods available to analyze these trajectories, but in the epidemiologic literature, most of those are never used. The purpose of the present study is to compare five statistical methods to detect developmental trajectories in a longitudinal epidemiological data set.
All five statistical methods (K-means clustering, a “two-step” approach with mixed modeling and K-means clustering, latent class analysis [LCA], latent class growth analysis [LCGA], and latent class growth mixture modeling [LCGMM]) were performed on a real-life data set and two manipulated data sets. The first manipulated data set contained four different linear developments over time, whereas the second contained two linear and two quadratic developments.
For the real-life data set, all five classification methods revealed comparable trajectories. Regarding the manipulated data sets, LCGA performed best in detecting linear trajectories, whereas none of the methods performed well in detecting a combination of linear and quadratic trajectories. Furthermore, the optimal solution for LCA and LCGA contained more classes compared with LCGMM.
Although LCGA and LCGMM seem to be preferable above the more simple methods, all classification methods should be applied with great caution.
Publisher
Elsevier Inc,Elsevier,Elsevier Limited
