I am working on a longitudinal study with 140 participants divided in 3 groups. The participants were assessed every 2 years from 2010 (4 time-points in total), so time-points are equally distributed, but there are some dropouts, so some patients are missing some time-point.
The assessment consisted of some tests, the results of which are discrete numerical variables (e.g. one of these is the MoCA test, which is a cognitive test with different tasks and for each task the participant is given a score; the final score is the sum of the partial scores).
My goal would be to show any difference between groups in the progression of the scores through time.
After some readings I am thinking to use a mixed effect model with the random part on the single individual level and the fixed part on the group level, would that make sense? What other statistical model could I use?
how about time series clustering (package tsclust in R)? Also, be aware that correlation or regression-based tests on multiple time series that have the same underlying structure (eg temporal autocorrelation) may produce inflated p values.
You can also use a multi-level analysis via MLWin. As Giorgio said, you must be aware of the correlation, however multi-level analysis deals with this correlation on its core.
In this case, the individuals repeated measures (scores) will be level 1 variables, while the individuals themselves will be level 2 variables.
I agree with both Cauane and Georgio above.
You are dealing with a multi-level analysis of panel data with 4 repeated measures.
When it comes to the wonderful stats package, R, it's a fair bet that someone has faced a similar problem and shared their solutions. See the link attached: Multilevel analysis: panel data and multiple levels.
http://stats.stackexchange.com/questions/172520/multilevel-analysis-panel-data-and-multiple-levels
Thank you Cauane, Giorgio and Hume, I will try using what you suggest and get back here with my results in case someone else is interested in the question.
These resources should help
http://www.bristol.ac.uk/cmm/software/mlwin/mlwin-resources.html#multipleresp
this one is a very good starting point
http://www.bristol.ac.uk/media-library/sites/cmm/migrated/documents/repeated-measures.pdf
Thank you very much to everybody for the help. For anybody interested I am adding here a link to the discussion I started on statalist asking for specifics about syntax.
http://www.statalist.org/forums/forum/general-stata-discussion/general/1370567-command-for-multilevel-mixed-effect-model
I would very strongly recommend you look at this
http://www.pilesofvariance.com/
which has syntax for many software environments including Stata
these are the chapters
Table of Contents
Section I: Building Blocks for Longitudinal Analysis
CHAPTER 1: Introduction to the Analysis of Longitudinal Data
CHAPTER 2: Between-Person Analysis and Interpretation of Interactions
CHAPTER 3: Introduction to Within-Person Analysis and Model Comparisons
Section II: Modeling the Effects of Time
CHAPTER 4: Describing Within-Person Fluctuation over Time
CHAPTER 5: Introduction to Random Effects of Time and Model Estimation
CHAPTER 6: Describing Within-Person Change over Time
Section III: Modeling the Effects of Predictors
CHAPTER 7: Time-Invariant Predictors in Longitudinal Models
CHAPTER 8: Time-Varying Predictors in Models of Within-Person Fluctuation
CHAPTER 9: Time-Varying Predictors in Models of Within-Person Change
Section IV: Advanced Applications
CHAPTER 10: Analysis over Alternative Metrics and Multiple Levels of Time
CHAPTER 11: Analysis of Individuals within Groups over Time
CHAPTER 12: Analysis of Repeated Measures Designs Not Involving Time
CHAPTER 13: Additional Considerations and Future Directions
Hi
I collected longitudinal data with irregular time intervals. I collected data from mobile app users in three waves of surveys soon after they finished using the app. Can you suggest the best method for analysing longitudinal data with uneven time interval? and the software which can be used for such analysis?
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