Ceyda Oksel posted on Thursday, August 03, 2017 - 8:24 am
Iím working with longitudinal data coming from multiple cohorts. The indicator variables represent presence/absence of wheeze in a child from birth to 16 years of age. I'd like to assign children into different groups with similar patterns of wheezing. The problem is that data collection frequency and time points are quite different in each cohort. I performed LCA using common ages that are approximately shared across all cohorts with and without a covariate (cohort type) but that way thousands of data points are ruled out (eg. in one cohort, data available at month 6, 12, 18 but in others there is only month12 data so month6 and 18 data are excluded).
Do you have any suggestions as to how I can make use of all existing data during LCA without extensive data imputation?
Ceyda Oksel posted on Friday, August 04, 2017 - 3:02 am
I've checked it but I don't think it's suitable for me because what I precisely need is a model that can accommodate multiple subgroups, binary indicators, categorical latent variables and subgrouply-varying times of observations.
Check the output to see how many dimensions of integration you have (I assume 3) - you find that in the initial Summary.
Ask for TECH8 and check if you have any negative ABS changes in which case you need more precision in your integration - this is done by asking for say integration = 30 or MonteCarlo integration (5000); see UG.