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I would like to compare parameters across groups in a dataset which I have used multiple imputation (via aregImpute). First, several continuous parameters are such that I would like to use the non-parametric Kruskal-Wallis test, however I don't see this as an option using fit.mult.impute in rms. Second, I also am not finding an easy way to use fit.mult.impute for a Chi-square test for the categorical parameters. Perhaps I am overlooking something or approaching this incorrectly? Essentially what I have is below:

imputed_data <- aregImpute(~ PARAMS, n.impute=100, nk=4, 
                           data=df,type = "pmm", match = "weighted",
                           burnin = 100)

I would like to then use fit.mult.impute to generate pooled estimates for both the Kruskal-Wallis and Chi-square tests across a grouping parameter in the data:

model_cont <- fit.mult.impute(continuous_parameter ~ group_parameter,
                              fitter = ???, xtrans = imputed_data, 
                              data = df)

model_cat <- fit.mult.impute(categorical_parameter ~ group_parameter,
                             fitter = ???, xtrans = imputed_data, 
                             data = df)

Any thoughts on how to accomplish this? Thank you!

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closed as off-topic by whuber Jan 11 at 2:09

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  • $\begingroup$ Pinging @Frank Harrell $\endgroup$ – Ben Jan 11 at 1:46