Bootstrap_Variance {StackImpute}

Bootstrap_Variance

Description

This function takes a dataset with stacked multiple imputation and a model fit and applies bootstrap to estimate the covariance matrix accounting for imputation uncertainty.

Usage

Bootstrap_Variance(fit, stack, M, n_boot = 100)

Arguments

fit

object with corresponding vcov method (e.g. glm, coxph, survreg, etc.) from fitting to the (weighted) stacked dataset

stack

data frame containing stacked dataset across multiple imputations. Could have 1 or M rows for each subject with complete data. Should have M rows for each subject with imputed data. Must contain the following named columns: (1) stack$.id, which correspond to a unique identifier for each subject. This column can be easily output from MICE. (2) stack$wt, which corresponds to weights assigned to each row. Standard analysis of stacked multiple imputations should set these weights to 1 over the number of times the subject appears in the stack. (3) stack$.imp, which indicates the multiply imputed dataset (from 1 to M). This column can be easily output from MICE.

M

number of multiple imputations

n_boot

number of bootstrap samples

Details

This function implements the bootstrap-based estimation method for stacked multiple imputations proposed by Dr. Paul Bernhardt in “A Comparison of Stacked and Pooled Multiple Imputation" at the Joint Statistical Meetings, 2019.

Value

Variance, estimated covariance matrix accounting for within and between imputation variation

Examples

data(stackExample)

fit = stackExample$fit
stack = stackExample$stack

bootcovar = Bootstrap_Variance(fit, stack, M = 5, n_boot = 10)
VARIANCE_boot = diag(bootcovar)