CODEWITHSUNDEEP
rlogistic-regressionmissing-dataimputationr-mice
user81715
user81715

Reputation: 67

Binary logistic regression with multiply imputed data

I have been trying to work with options available within R (i.e. MICE) to do binary logistic regression analyses (with interaction between continuous and categorical predictors).

However, I am struggling to carry out this simple analysis on multiply imputed data (details and reproducible example here).

Specifically, I have not been able to figure out a way to pool every aspect of the output including an equivalence of 'log likelihood ratio' using the GLM function of Mice.

To avoid redundancy from a previous post, I am seeking ANY suggestions for R packages or other softwares that may make it easy/possible to pool all essential components of the output for binary logistic regression (i.e. equivalent of model likelihood ratio test, regression coefficients, wald test). See below for an example that I was able to obtain using rms on a non-imputed data (could not figure out a way to run this on multiply imputed data)

> mylogit
 Frequencies of Missing Values Due to Each Variable
 P1    ST   P8 
 18    0   31 

 Logistic Regression Model

 lrm(formula = P1 ~ ST + P8 + ST * P8, data = PS, x = TRUE, 
 y = TRUE)


 Model Likelihood     Discrimination    Rank Discrim.    
  Ratio Test           Indexes           Indexes       
  Obs           362    LR chi2     18.34    R2       0.077    C       0.652    
  0            287    d.f.            9    g        0.664    Dxy     0.304    
  1             75    Pr(> chi2) 0.0314    gr       1.943    gamma   0.311    

  max |deriv| 8e-08    gp     0.099    tau-a   0.100       Brier    0.155                     

                      Coef    S.E.   Wald Z Pr(>|Z|)
 Intercept          -0.5509 0.3388 -1.63  0.1040  
 ST=       2      -0.5688 0.4568 -1.25  0.2131  
 ST=       3      -0.7654 0.4310 -1.78  0.0757  
 ST=       4      -0.7995 0.5229 -1.53  0.1263  
 ST=       5      -1.2813 0.4276 -3.00  0.0027  
 P8                 0.2162 0.4189  0.52  0.6058  
 ST=       2 * P8 -0.1527 0.5128 -0.30  0.7659  
 ST=       3 * P8 -0.0461 0.5130 -0.09  0.9285  
 ST=       4 * P8 -0.5031 0.5635 -0.89  0.3719  
 ST=       5 * P8  0.3661 0.4734  0.77  0.4393

In sum, my question is: 1) package/software that is capable of handling multiply imputed data to complete a traditional binary logistic regression analysis, esp with interaction term 2) possible steps I need to take to do run the analysis in that program

Upvotes: 0

Views: 1779

Answers (1)

Lucy
Lucy

Reputation: 991

The rms package has great features for combining multiply imputed data using the fit.mult.impute() function. Here is a small working example:

dat <- mtcars
## introduce NAs
dat[sample(rownames(dat), 10), "cyl"] <- NA
im <- aregImpute(~ cyl + wt + mpg + am, data = dat)
fit.mult.impute(am ~ cyl + wt + mpg, xtrans = im, data = dat, fitter = lrm)

Upvotes: 2

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