| Title: | Balance Check for Multiple Covariates in Matched Observational Studies |
|---|---|
| Description: | Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies. |
| Authors: | Hao Chen and Dylan Small |
| Maintainer: | Hao Chen <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.2 |
| Built: | 2024-12-27 06:15:27 UTC |
| Source: | CRAN |
This package includes two tests for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.
Hao Chen and Dylan Small
Maintainer: Hao Chen ([email protected])
Chen, H. and Small, D. (2019) New multivariate tests for assessing covariate balance in matched observational studies.
This function tests whether covariates in a treatment group and a matched control group are balanced in observational studies through the minimum spanning tree constructed on the subjects.
CrossMST(distM,treated.index,perm=0,k=1,discrete.correction=TRUE)CrossMST(distM,treated.index,perm=0,k=1,discrete.correction=TRUE)
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-MST 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. |
test.stat.Z |
The standardized test statistic (ZR 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. |
Chen, H. and Small, D. (2019) New multivariate tests for assessing covariate balance in matched observational studies.
## 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) CrossMST(smokingDist, 1:150) ## Uncomment the following line to get permutation p-value with 1,000 permutations. # CrossMST(smokingDist, 1:150, perm=1000)## 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) CrossMST(smokingDist, 1:150) ## Uncomment the following line to get permutation p-value with 1,000 permutations. # CrossMST(smokingDist, 1:150, perm=1000)
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.
CrossNN(distM,treated.index,perm=0,k=1,discrete.correction=TRUE)CrossNN(distM,treated.index,perm=0,k=1,discrete.correction=TRUE)
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. |
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. |
## 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)## 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)
This function calculates D11 and D22 for the CrossNN test.
getD(distM,treated.index)getD(distM,treated.index)
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. |
This function calculates D11 and D22 for the CrossNN test when k-NN (k>1) is used.
getDk(distM,treated.index,k)getDk(distM,treated.index,k)
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. |
k |
An integer larger than 1. |
This function calculates R1 and R2 for the CrossMST test.
getR1R2(E,treated.index)getR1R2(E,treated.index)
E |
A matrix with the number of rows the number of edges in the MST and 2 columns. Each row records the subject indices of the two ends of an edge in the MST. |
treated.index |
The subject indices of the treated subjects. |
This is a 300 by 300 distance matrix with the indices of the treated subjects 1:150 and the indices of the matched controls 151:300. These treated subjects and matched controls are a subset of the original dataset used in the reference paper with 679 treated subjects and 679 matched controls.