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From your description, you might be better off doing imputation on all your observations. There is no need to remove cases with missing outcome values, as analysis of properly performed multiply imput …

"MICE" stands for "multiple imputation via chained equations," one particular way to do imputation. It might help to keep that distinction in mind. … There is random sampling involved in the MICE algorithm (Section 4.5 of FIMD). …

The simplest way to deal with this is to recognize that the correlation coefficient of 0.50 estimated from 49 observations (as described in comments) is quite compatible with the value of 0.39 that yo …

First, although multiple imputation is often highly favored, it isn't necessary if the fraction of missing data is small. Then single imputation might be adequate in practice. See Section 3.11 of Fran …

Here, how can I "pool" or build one dataset on which I can perform the mean and then use that data set into the cox model? The way to proceed isn't to pool the datasets, it's to pool the model resul …

There are two separate issues here. First is the problem of getting coefficient covariance matrices from a weighted multivariate (in the sense of multiple outcomes) regression. The second is how to ap …

You could try to apply Rubin's rules directly to the results of the multivariate regressions on each of the imputed data sets. You average the coefficient matrices (from coef() on each model) to get t …

You will not be better off categorizing your continuous predictor variables. This is discussed in many places on this site, for example on this page. With continuous predictors you can evaluate their …

One solution would be to use settings and imputation methods other than the defaults in mice to deal with that constraint. … The mice package itself provides a mice.impute.passive() function that performs calculations based on the imputed data. …

You might also want to look at the mice package for the imputation part of the problem; rms can handle objects produced by mice. …

For example, from the code for mice:::summary.mira() where object is a mira object and v is the set of model fits after tidy() is applied: if (!" … The mice:::pool.fitlist() function calls summary(), so you get the same warnings. …

I got a warning related to Date objects when I tried to use all the columns, so omit the first 3. library(mice) library(rms) ## also loads Hmisc, survival, etc. ddJ <- datadist(jasa[,4:14]) options(datadist … You can ask the mice package to retrieve a list of all 10 imputed data sets. c1 <- complete(m1,action="all") As you might expect, the coefficients reported for models are the averages of those for the …

Third, the functions used by the mice package can be quite picky about the way that model objects are constructed. … Make sure to read the documentation very carefully; make sure that all required packages are installed and working properly and that the model objects you feed to functions in mice have the values and …

It sounds like the "linear combination" you want to calculate is a predicted outcome (log-odds, in your case) based on the modeled coefficient estimates and hypothesized predictor values. If you are w …

Second, to answer your question, so far as I know the pool() function in the mice package does not pool random effects across the multiple models. …