Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details.status: publishe
Longitudinal data arise when individuals are measured several times during an ob- servation period a...
In the health and social sciences, longitudinal data have often been analyzed without taking into ac...
Structural equation modeling is a common technique to assess change in longitudinal designs. However...
Although many books currently available describe statistical models and methods for analyzing longit...
Longitudinal data sets consist of repeated observations of an outcome over time, and a corresponding...
Longitudinal data sets consist of repeated observations of an outcome over time, and a corresponding...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
This paper is a review of statistical techniques for studying longitudinal data. In particular, we o...
This thesis treats a series of models useful to analyze multivariate longitudinal data collected fro...
Much effort has been made to develop models for longitudinal data analysis, but comparably less atte...
Replication of research findings across independent longitudinal studies is essential for a cumulati...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
[[abstract]]Longitudinal study has become one of the most commonly adopted designs in medical resear...
This is the first book in longitudinal categorical data analysis with parametric correlation models ...
Longitudinal data arise when individuals are measured several times during an ob- servation period a...
In the health and social sciences, longitudinal data have often been analyzed without taking into ac...
Structural equation modeling is a common technique to assess change in longitudinal designs. However...
Although many books currently available describe statistical models and methods for analyzing longit...
Longitudinal data sets consist of repeated observations of an outcome over time, and a corresponding...
Longitudinal data sets consist of repeated observations of an outcome over time, and a corresponding...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
This paper is a review of statistical techniques for studying longitudinal data. In particular, we o...
This thesis treats a series of models useful to analyze multivariate longitudinal data collected fro...
Much effort has been made to develop models for longitudinal data analysis, but comparably less atte...
Replication of research findings across independent longitudinal studies is essential for a cumulati...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
The article develops marginal models for multivariate longitudinal responses. Overall, the model con...
[[abstract]]Longitudinal study has become one of the most commonly adopted designs in medical resear...
This is the first book in longitudinal categorical data analysis with parametric correlation models ...
Longitudinal data arise when individuals are measured several times during an ob- servation period a...
In the health and social sciences, longitudinal data have often been analyzed without taking into ac...
Structural equation modeling is a common technique to assess change in longitudinal designs. However...