Version:	1.0-4
Title:	Wilcoxon Rank Tests for Clustered Data
Description:	Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.
License:	GPL (≥ 3)
Depends:	R (≥ 4.0)
Imports:	stats, MASS, Rcpp (≥ 1.0.5)
LinkingTo:	Rcpp
LazyData:	true
URL:	https://github.com/wenjie2wang/clusrank
BugReports:	https://github.com/wenjie2wang/clusrank/issues
Encoding:	UTF-8
RoxygenNote:	7.3.1
NeedsCompilation:	yes
Packaged:	2024-02-17 16:04:57 UTC; wenjie
Author:	Wenjie Wang [cre, ctb], Yujing Jiang [aut], Mei-Ling Ting Lee [ctb], Jun Yan [ctb]
Maintainer:	Wenjie Wang <wang@wwenjie.org>
Repository:	CRAN
Date/Publication:	2024-02-17 16:40:02 UTC

CARMS scores

Description

A data set from a research on complement factor H R1210C rare variant and its associated phenotype. This data set contains Clinical Age-Related Maculopathy Staging (CARMS) scores from a total of 143 patients (283 eyes) including 62 patients with the rare variant. The data is from the lab of Dr. Johanna M. Seddon,

Format

A data frame with 283 rows and 7 variables.

• ID patient identifier

• Eye OD (right eye), OS(left eye)

• Variant 1: No R1210C Variant; 2: R1210C Variant

• CARMS Patient's last CARMS grade, related to age-related macular degeneration (AMD). 1: no AMD; 2: early AMD; 3: intermediate AMD; 4: geographic atroph(advanced dry); 5: neovascular disease (advanced wet)

• Age_group 1: < 70 years; 2: 70 to 79.9 years; 3: >= 80 years

• Sex 1: male; 2: female

• AgeSex 1: agegroup = 1, sex = 1; 2: agegroup = 2, sex = 1; 3: agegroup = 3, sex = 1; 4: agegroup = 1, sex = 2; 5: agegroup = 2, sex = 2; 6: agegroup = 3, sex = 2;

Note

CARMS grades were assessed separately for the two advanced stages (4 and 5): 1. CARMS 1,2,3, and 4 was assessed; 2. CARMS 1,2,3, and 5 was assessed

Source

The data came from Seddon's lab.

References

Seddon JM, Sharma S, Adelman RA (2006) Evaluation of the Clinical Age-related Maculopathy Staging System. Ophthalmology, 113, 260-266.

Ferrara D, Seddon JM (2015) Phenotypic characterization of complement factor H R1210C rare genetic variant in age-related macular degeneration JAMA Ophthalmol, 2015 Apr 16. doi:10.1001/jamaophthalmol.2015.0814.

Examples

data(amd)
clusWilcox.test(CARMS ~ Variant + cluster(ID), data = amd,
               subset = CARMS %in% c(1, 2, 3, 4), method = "rgl", alternative = "two")
clusWilcox.test(CARMS ~ Variant + cluster(ID), data = amd,
               subset = CARMS %in% c(1, 2, 3, 4), method = "ds", alternative = "two")
clusWilcox.test(CARMS ~ Variant + cluster(ID) + stratum(AgeSex), data = amd,
               subset = CARMS %in% c(1, 2, 3, 4), alternative = "two")

Wilcoxon Rank Sum and Signed Rank Test for Clustered Data

Description

Performs one-sample and two-sample Wilcoxon test for clustered data on vectors of data.

Usage

clusWilcox.test(x, ...)

## S3 method for class 'formula'
clusWilcox.test(
  formula,
  data = parent.frame(),
  subset = NULL,
  na.action = na.omit,
  alternative = c("two.sided", "less", "greater"),
  mu = 0,
  paired = FALSE,
  exact = FALSE,
  B = 2000,
  method = c("rgl", "ds", "dd"),
  ...
)

## Default S3 method:
clusWilcox.test(
  x,
  y = NULL,
  cluster = NULL,
  group = NULL,
  stratum = NULL,
  data = NULL,
  alternative = c("two.sided", "less", "greater"),
  mu = 0,
  paired = FALSE,
  exact = FALSE,
  B = 2000,
  method = c("rgl", "ds", "dd"),
  ...
)

Arguments

Details

The formula interface is to both clustered signed rank test and clustered rank sum test.

The default of cluster id is that there is one member in each cluster. Both balanced data (identical cluster size) and unbalanced data (different cluster sizes) are supported in all tests provided in this package. For clustered rank sum test, the data can either have treatment assigned at cluster level or individual level.

If both x and y are given or only x is given and paired is TRUE, a clustered Wilcoxon signed rank test of the null that the distribution of x - y (paired sample) or of x (one sample) is symmetric about mu is performed.

Otherwise, if only x is given and paired is FALSE, a Wilcoxon rank sum test is performed. In this case, measurements from different treatment groups should be combined in x and the group variable is required. When there are two treatment groups, the null is that the distributions of values from the two groups differ by a location shift of mu and the alternative is that they differ by some other location shift. When there are m (>= 2) treatment groups, ds method can test if the location of the m groups are identical or not.

For RGL rank sum test when treatment is assigned at cluster level, an extra stratification variable is allowed through stratum.

The exact test is only available for RGL signed rank test and RGL rank sum test when treatment is assigned at cluster level.

Value

A list with class "htest" containing the following components, for different test the components may vary:

Methods (by class)

• clusWilcox.test(formula): S3 method for class 'formula'

• clusWilcox.test(default): Default S3 method.

Warning

This function can use large amounts of memory and stack if 'exact = TRUE' and one sample is large (and even crash R if the stack limit is exceeded). Not recommended for data set with number of clusters more than 50.

Author(s)

Yujing Jiang

References

Bernard Rosner, Robert J. Glynn, Mei-Ling T. Lee (2006) The Wilcoxon Signed Rank Test for Paired Comparisons of Clustered Data. Biometrics, 62, 185-192.

Bernard Rosner, Robert J. Glynn, Mei-Ling T. Lee (2003) Incorporation of Clustering Effects for the Wilcoxon Rank Sum Test: A Large-Sample Approach. Biometrics, 59, 1089-1098.

Bernard Rosner, Robert J. Glynn, Mei-Ling T. Lee (2006) Extension of the Rank Sum Test for Clustered Data: Two-Group Comparisons with Group. Biometrics, 62, 1251-1259.

Somnath Datta, Glen A. Satten (2005) Rank-Sum Tests for Clustered Data. Journal of the American Statistical Association, 100, 908-915.

Somath Datta, Glen A. Satten (2008) A Signed-Rank Test for Clustered Data. Biometrics, 64, 501-507.

Sandipan Dutta, Somnath Datta (2015) A Rank-Sum Test for Clustered Data When the Number of Subjects in a Group within a Cluster is Informative. Biometrics, 72, 432-440.

Examples

library(clusrank)

## Clustered signed rank test using RGL method.
data(crsd)
clusWilcox.test(z, cluster = id, data = crsd, paired = TRUE, method = "rgl")
## or
clusWilcox.test(z ~ cluster(id), data = crsd, paired = TRUE, method = "rgl")

## Clustered rank sum test using RGL method.
data(crd)
clusWilcox.test(z ~ group + cluster(id), data = crd, method = "rgl")
## or
clusWilcox.test(z, cluster = id, group = group, data = crd, method = "rgl")

Identify clusters

Description

This is a special function used in the context of formula used for Wilcoxon sum rank test for clustered data. It identifies the cluster id of observations, and is used on the right hand side of a formula.

Usage

cluster(x)

Arguments

Details

THe function's only action is semantic, to mark a variable as the cluster indicator. If not supplied, will assume no cluster in the data.

Value

x

See Also

clusWilcox.test.formula

Clustered Non-Stratified Data for Testing Clustered Rank Sum Test

Description

Non-stratified data including 3 variables: id, z, group. Have some missing values.

Format

A data frame with 340 observations on 3 variables.

[, 1] id cluster id

[, 2] z score

[, 3] group group id

Source

https://sites.google.com/a/channing.harvard.edu/bernardrosner/channing/incorporating-cluster-effects-for-the-wilcoxon-rank-sum-test-1/cluswilcox/sample_data

Clustered Stratified Data for Testing the Clustered Rank Sum Test

Description

Stratified data with clustering structure, Have some missing values.

Format

A data frame with 956 observations on 4 variables.

[, 1] id cluster id

[, 2] z score

[, 3] group group id

[, 4] stratum stratum id

Source

https://sites.google.com/a/channing.harvard.edu/bernardrosner/channing/incorporating-cluster-effects-for-the-wilcoxon-rank-sum-test-1/stratify–cluswilcox/sample-data

Difference Between Pre and Post Treatment Scores with Clustering Structure: Balanced

Description

Simulated clustered data with the difference of pre and post treatment scores recorded. The data is balanced.

Format

A data frame with 40 rows and 2 variables.

[, 1] id cluster id

[, 2] z score difference

Source

https://sites.google.com/a/channing.harvard.edu/bernardrosner/channing/cluster-signed-rank-test-for-balancedunbalanced-design/sample_balanced_data

Difference between Pre and Post Treatment Scores with Clustering Structure: Unbalanced

Description

Simulated clustered data with the difference of pre and post treatment scores recorded. The data is unbalanced.

Format

A data frame with 748 rows and 2 variables.

[, 1] id cluster id

[, 2] z score difference

Source

https://sites.google.com/a/channing.harvard.edu/bernardrosner/channing/cluster-signed-rank-test-for-balancedunbalanced-design/sample-data-for-unbalanced-data-1

Pairwise Wilcoxon Rank Sum and Signed Rank Tests for Clustered Data

Description

Performs pairwise comparisons between group levels with corrections for multiple testing.

Usage

pairwise.clusWilcox.test(
  x,
  group,
  cluster,
  p.adjust.method = p.adjust.methods,
  ...
)

Arguments

Value

A pairwise.htest object.

Examples

library(clusrank)
data(crd)

## for demonstration purpose, create random groups
set.seed(123)
g <- sample(seq_len(3), size = nrow(crd), replace = TRUE)
with(crd, pairwise.clusWilcox.test(z, group = g, cluster = id,
                                   method = "ds", p.adjust.method = "fdr"))

Identify strata.

Description

This is a special function used in the context of formula used for Wilcoxon sum rank test for clustered data. It identifies the stratum id of observations, and is used on the right hand side of a formula.

Usage

stratum(x)

Arguments

Details

THe function's only action is semantic, to mark a variable as the stratum indicator. If not supplied, will assume no stratification in the data.

See Also

clusWilcox.test.formula