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| Dylan John posted on Sunday, September 03, 2017 - 11:30 am
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I am currently running a 2 timepoint LTA and using the identified transition groups to predict suicidality (dichotomous variable).
When I conducted my LPA I used maximum likelihood estimation to account for missing data, and only included participants that had complete data on at least one indicator variable.
Similarly, I have used full information maximum likelihood estimation in my LTA analysis to account for missingness on my latent profiles at each timepoint.
Where I am seeking guidance is how I should handle (1) my covariates in the LTA, as well as (2) my outcome. For the outcome, I believe I will just remove cases who are missing and then compare them to those who were kept in on sociodemographics and exposures. But, in relation to covariate, what do you recommend I do? Can you account for missingness in covariates using MLE the same way that I did for my latent profiles? |
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| The 1-step LTA-covariate analysis can bring the covariates into the model, but their missingness results in dimensions of integration and makes for heavy computations. |
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| Dylan John posted on Monday, September 04, 2017 - 3:23 pm
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| How would you suggest I proceed? Would you look to impute the covariates elsewhere and import back into Mplus? |
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| It's a difficult situation for which I don't have a good suggestion. Imputation has limited options in any subsequent analysis steps. Bayes is an alternative approach which handles missing on covariates better (see chapter 9 of our RMA book) but Bayes is not always easy to use with mixtures due to label switching (see our Bayes workshop videos). |
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| Dylan John posted on Wednesday, September 06, 2017 - 2:42 pm
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| Thanks for your feedback. Would you happen to have any good literature references that describe the issues of missingness in mixture modelling? |
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| No, I don't. You may want to try SEMNET. |
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