Hello,
I am working with data from a randomized control trial, where the primary outcome is to assess infant length-for-age z-scores (LAZ) at 1 year of age (comparison between treatment groups using ANOVA). Although the main analysis will follow an intention-to-treat approach, as a sensitivity analysis we want to impute LAZ at 12 months, and run the ANOVA again with imputed values. The trial is still ongoing (almost 80% complete), but so far there is not a lot of missing 12-month LAZ data (<4%).
LAZ is measured at various time points (birth, 3 months, 6 months, 9 months) before the final endpoint (12 months). In the imputation model, I want to use these prior measurements to help predict 12-month LAZ. Measurements at these time points are also not complete- they include a similar proportion of missing data (<5%). Ideally, I would like for the imputation model to account for clustering by individuals (due to various length measurements at different time points). Is there a way to do this??
I have researched extensively and cannot seem to find a good approach. Many resources don't account for clustering in the imputation stage but then run a multilevel model in the estimation stage, which is not what I want to do. Other references also suggest organizing the data in wide form, and making each measurement time point a separate variable (e.g., LAZ0, LAZ3, LAZ6, etc.), then use a MICE model (mi impute chained). However, I don't think this accounts for clustering by participant.
Any help is appreciated, thank you!
I am working with data from a randomized control trial, where the primary outcome is to assess infant length-for-age z-scores (LAZ) at 1 year of age (comparison between treatment groups using ANOVA). Although the main analysis will follow an intention-to-treat approach, as a sensitivity analysis we want to impute LAZ at 12 months, and run the ANOVA again with imputed values. The trial is still ongoing (almost 80% complete), but so far there is not a lot of missing 12-month LAZ data (<4%).
LAZ is measured at various time points (birth, 3 months, 6 months, 9 months) before the final endpoint (12 months). In the imputation model, I want to use these prior measurements to help predict 12-month LAZ. Measurements at these time points are also not complete- they include a similar proportion of missing data (<5%). Ideally, I would like for the imputation model to account for clustering by individuals (due to various length measurements at different time points). Is there a way to do this??
I have researched extensively and cannot seem to find a good approach. Many resources don't account for clustering in the imputation stage but then run a multilevel model in the estimation stage, which is not what I want to do. Other references also suggest organizing the data in wide form, and making each measurement time point a separate variable (e.g., LAZ0, LAZ3, LAZ6, etc.), then use a MICE model (mi impute chained). However, I don't think this accounts for clustering by participant.
Any help is appreciated, thank you!
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