Variable Selection for Multiply Imputed Data

Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. ‘miselect’ presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes. They, by construction, force selection of the same variables across multiply imputed data. ‘miselect’ also provides cross validated variants of these methods.

Installation

miselect can installed from Github via

# install.packages("devtools")
devtools::install_github("umich-cphds/miselect", build_opts = c())

The Github version may contain bug fixes not yet present on CRAN, so if you are experiencing issues, you may want to try the Github version of the package.

Example

Here is how to use cross validated saenet. A nice feature of saenet is that you can cross validate over alpha without having to use foldid.

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

set.seed(48109)
# Using the mice defaults for sake of example only.
mids <- mice(miselect.df, m = 5, printFlag = FALSE)
dfs <- lapply(1:5, function(i) complete(mids, action = i))

# Generate list of imputed design matrices and imputed responses
x <- list()
y <- list()
for (i in 1:5) {
    x[[i]] <- as.matrix(dfs[[i]][, paste0("X", 1:20)])
    y[[i]] <- dfs[[i]]$Y
}

# Calculate observational weights
weights  <- 1 - rowMeans(is.na(miselect.df))
pf       <- rep(1, 20)
adWeight <- rep(1, 20)
alpha    <- c(.5 , 1)

# Since 'Y' is a binary variable, we use 'family = "binomial"'
fit <- cv.saenet(x, y, pf, adWeight, weights, family = "binomial",
                 alpha = alpha)

# By default 'coef' returns the betas for (lambda.min , alpha.min)
coef(fit)
#> (Intercept)          X1          X2          X3          X4          X5 
#>  0.03873967  1.12186207  0.81021394  0.00000000  1.71529730  0.07858127 
#>          X6          X7          X8          X9         X10         X11 
#> -0.32964553  1.77355578  0.15790191 -0.14994831  0.22963563  0.90667310 
#>         X12         X13         X14         X15         X16         X17 
#>  0.00000000  0.31233789 -0.11647152 -0.42321068 -0.01911678  0.28367510 
#>         X18         X19         X20 
#>  0.09481255 -0.34254703  0.18431795

You can supply different values of lambda and alpha. Here we use the lambda and alpha selected by the one standard error rule

coef(fit, lambda = fit$lambda.1se, alpha = fit$alpha.1se)
#> (Intercept)          X1          X2          X3          X4          X5 
#>   0.1680638   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000 
#>          X6          X7          X8          X9         X10         X11 
#>   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000 
#>         X12         X13         X14         X15         X16         X17 
#>   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000   0.0000000 
#>         X18         X19         X20 
#>   0.0000000   0.0000000   0.0000000

Bugs

If you encounter a bug, please open an issue on the Issues tab on Github or send us an email.

Contact

For questions or feedback, please email Jiacong Du at jiacong@umich.edu or Alexander Rix alexrix@umich.edu.

References

Du, J., Boss, J., Han, P., Beesley, L. J., Kleinsasser, M., Goutman, S. A., … & Mukherjee, B. (2022). Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods. Journal of Computational and Graphical Statistics, 31(4), 1063-1075.