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Michael Lap posted on Wednesday, March 21, 2018  2:42 pm



In a linear regression, the relationship between the predictor and the outcome is quadratic (looks like a curve and a quadratic coefficient is strongly significant). 1) If I use this predictor as a covariate and the outcome variable as a variable to form class trajectories in LCGA, would the quadratic relationship between them be a problem? In other words, does LCGA require linearity b/w covariates and predicted class trajectories? All other covariates are nominal (binary). 2) If nonlinearity is a problem, we can split the continuous covariate into categories if needed. Thank you! 


Is the covariate a timevarying covariate or not? Is the growth linear or quadratic? 

Michael Lap posted on Wednesday, March 21, 2018  3:54 pm



Bengt, the covariate is actually timevarying but it is strongly correlated with the outcome at each time point (we have 4 timepoints). So they correlate at T1, T2, T3, and T4 at .7.8 Therefore, we included just the T1 covariate as one of predictors of time trajectories. The trajectories are more nonlinear than linear but we struggle to model the quadratic pattern. So we have a linear trend T1T3 and then a slight increase from T3 to T4. 


So you can have a regular multinomial logistic regression model C on T1; and have a piecewise linear for the growth part. The quadratic effect of T1 on the outcomes could then be reflected by say high T1 values making a C class with high secondpiece slope more likely. 

Michael Lap posted on Thursday, March 22, 2018  4:07 pm



Thank you, Brent! the quadratic part now works, I figured it out. The quadratic slope is significant in 2 out of 3 class trajectories. So If I include the quadratic or piecewise part, then the quadratic relationship b/w the covariate measured at T1 only and the latent trajectories will be reflected in the "high T1 values making a C class with high secondpiece slope more likely", right? 

Michael Lap posted on Thursday, March 22, 2018  4:13 pm



Bengt, Sorry, reading your post again: C on T1 (and other covariates) is reflected in the multinomial part of the output, right? Just want to make sure I understand you correctly. 


Right on both posts. 

Michael Lap posted on Thursday, March 22, 2018  4:36 pm



Thank you, Bengt! 

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