Working together: mice and psfmi

Introduction

The mice function is one of the most used functions to apply multiple imputation. This page shows how functions in the psfmi package can be easily used in combination with mice. In this way multivariable models can easily be developed in combination with mice.

Installing the psfmi and mice packages

You can install the released version of psfmi with:

install.packages("psfmi")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("mwheymans/psfmi")

You can install the released version of mice with:

install.packages("mice")

Examples

mice and psfmi for pooling logistic regression models


  library(psfmi)
  library(mice)
#> 
#> Attaching package: 'mice'
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following objects are masked from 'package:base':
#> 
#>     cbind, rbind

  imp <- mice(lbp_orig, m=5, maxit=5) 
#> 
#>  iter imp variable
#>   1   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
  
  data_comp <- complete(imp, action = "long", include = FALSE)
  
  library(psfmi)
  pool_lr <- psfmi_lr(data=data_comp, nimp=5, impvar=".imp", 
                      formula=Chronic ~ Gender + Smoking + Function + 
                      JobControl + JobDemands + SocialSupport, method="D1")
  pool_lr$RR_model
#> $`Step 1 - no variables removed -`
#>            term     estimate  std.error  statistic       df      p.value
#> 1   (Intercept)  0.310513347 2.40148136  0.1293008 142.9509 0.8973014269
#> 2        Gender -0.338499204 0.41372330 -0.8181778 149.1700 0.4145607272
#> 3       Smoking  0.092810902 0.34042817  0.2726299 149.5391 0.7855138209
#> 4      Function -0.147834255 0.04353237 -3.3959619 145.9774 0.0008811736
#> 5    JobControl  0.005546417 0.01990332  0.2786678 126.3829 0.7809551903
#> 6    JobDemands -0.004218521 0.03859541 -0.1093011 104.1009 0.9131740942
#> 7 SocialSupport  0.044420991 0.05664118  0.7842526 140.1735 0.4342152092
#>          OR  lower.EXP   upper.EXP
#> 1 1.3641252 0.01183741 157.1996631
#> 2 0.7128393 0.31473815   1.6144847
#> 3 1.0972542 0.55997687   2.1500296
#> 4 0.8625741 0.79146520   0.9400717
#> 5 1.0055618 0.96672566   1.0459581
#> 6 0.9957904 0.92242077   1.0749958
#> 7 1.0454224 0.93467121   1.1692967

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mice and psfmi for selecting logistic regression models


  library(psfmi)
  library(mice)

  imp <- mice(lbp_orig, m=5, maxit=5) 
#> 
#>  iter imp variable
#>   1   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   1   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   2   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   3   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   4   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   1  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   2  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   3  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   4  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
#>   5   5  Carrying  Pain  Tampascale  Function  Radiation  Age  Satisfaction  JobControl  JobDemands  SocialSupport
  
  data_comp <- complete(imp, action = "long", include = FALSE)
  
  library(psfmi)
  pool_lr <- psfmi_lr(data=data_comp, nimp=5, impvar=".imp", 
                      formula=Chronic ~ Gender + Smoking + Function + 
                      JobControl + JobDemands + SocialSupport, 
                      p.crit = 0.157, method="D1", direction = "FW")
#> Entered at Step 1 is - Function
#> 
#> Selection correctly terminated, 
#> No new variables entered the model
  
  pool_lr$RR_model_final
#> $`Final model`
#>          term   estimate  std.error statistic       df     p.value        OR
#> 1 (Intercept)  1.1767415 0.46317343  2.540607 141.4734 0.012144970 3.2437871
#> 2    Function -0.1356213 0.04118315 -3.293125 142.9451 0.001248798 0.8731733
#>   lower.EXP upper.EXP
#> 1 1.2983651 8.1041570
#> 2 0.8049075 0.9472288

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