Longitudinal Data Analysis
1 Approaches for Modeling Longitudinal Data
2 Estimating Nonlinear Growth
There are a variety of ways to estimate nonlinear growth in a growth curve model using a mixed-effects or structural equation model:
- polynomial growth model
- fractional polynomial model (more parsimonious than traditional polynomials because can capture nonlinear growth with fewer parameters, thus reducing overfitting)
- piecewise/spline model
- can have fixed or random knots
- location of knots can be estimated for the data
- each individual can have a different numbers of knots and different location for the knots
- latent basis growth model
- can specify the rate of change between T1 and T2 to be one; can allow the rate of change to freely vary between remaining timepoints
- exponential growth model
- logistic growth model
- logarithmic growth model
- generalized additive model
- nonparametric growth model (e.g., kernel smoothing)
- Gompertz growth model
- Richards growth model
- Taylor series approximation model
- latent change score model
