CrossNN {BalanceCheck}

Covariate balance checking through the nearest neighbor graph

Description

This function tests whether covariates in a treatment group and a matched control group are balanced in observational studies through the nearest neighbor graph constructed on the subjects.

Usage

CrossNN(distM,treated.index,perm=0,k=1,discrete.correction=TRUE)

Arguments

distM

The distance matrix for the pooled observations (pooled over the treated subjects and the matched controls). If there are n treated subjects and n matched controls, then this distance matrix is a 2n by 2n matrix with the [i,j] element the distance between observation i and observation j. What distance to use is decided by users. Some simple choices are the Euclidean distance, L1 distance, and mahalanobis distance.

treated.index

The subject indices of the treated subjects. The subjects are ordered in the same way as for calculating the distance matrix, distM.

perm

The number of permutations performed to calculate the p-value of the test. The default value is 0, which means the permutation is not performed and only approximate p-value based on asymptotic theory is provided. Doing permutation could be time consuming, so be cautious if you want to set this value to be larger than 10,000.

k

Set as positive integer values, indicates k-NN is used.

discrete.correction

When this is set as TRUE (recommended), a continuation correction is done for computing the asymptotic p-value to account for the discrete nature of the statistic.

Value

test.stat.Z

The standardized test statistic (ZD in the reference paper.

pval.appr

The approximated p-value based on asymptotic theory.

pval.perm

The permutation p-value when argument 'perm' is positive.

See Also

CrossMST

Examples

## A snippet of the smoking example in the reference paper.
## smoking.rda contains a 300 by 300 distance matrix, smokingDist.
## The indices of the treated subjects are 1:150. 
data(smoking)
CrossNN(smokingDist, 1:150)

## Uncomment the following line to get permutation p-value with 1,000 permutations.
# CrossNN(smokingDist, 1:150, perm=1000)