Sampling weights and latent growth models

[Amy]

Hi everyone, 

Can the sampling.weights argument be used with the growth function for latent growth curve models where the dependent variable is measured more than once for each participant, but weights are based on baseline covariates only?

I am working with a dataset that is not representative of the target population in terms of a number of confounder variables. This dataset includes four repeated measurements for each participant, with some missingness in the data.

To evaluate the impact of selective participation, I was hoping to calculate sample weights (inverse participation probability scores), using variables harmonized across the dataset and a representative sample at baseline (e.g., baseline education level, sex, employment status). 

I see the growth function has the argument sampling.weights. However, in the description, it says "A variable name in the data frame containing sampling weight information. Currently only available for non-clustered data". 

As I have repeated measures for each participant, does this mean I should not apply weights using sampling.weights for my latent growth models? Are there any alternatives for improving sample representativeness?

Note that the code runs and I cannot see a relevant error message when I create a simulated weight variable and specify it for sampling.weights argument within the growth function.

Many thanks, 

Amy