Louis_Information: Louis_Information

In StackImpute: Tools for Analysis of Stacked Multiple Imputations

Description

This function takes a dataset with stacked multiple imputations and a glm or coxph fit and estimates the corresponding information matrix accounting for the imputation uncertainty.

Usage

Louis_Information(fit, stack, M, IMPUTED = NULL)

Arguments

fit	object of class glm or coxph 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.
M	number of multiple imputations
IMPUTED	deprecated parameter, not used in current version

Details

This function uses the observed information matrix principle proposed in Louis (1982) and applied to imputations in Wei and Tanner (1990). This estimator is a further extension specifically designed for analyzing stacks of multiply imputed data as proposed in Beesley and Taylor (2019) https://arxiv.org/abs/1910.04625.

Value

Info, estimated information matrix accounting for within and between imputation variation

Examples

data(stackExample)
Info = Louis_Information(stackExample$fit, stackExample$stack, M = 50)
VARIANCE = diag(solve(Info))

StackImpute documentation built on Sept. 10, 2021, 5:07 p.m.