htestClust: A Package for Marginal Inference of Clustered Data Under Informative Cluster Size

When observations are collected in/organized into observational units, within which observations may be dependent, those observational units are often referred to as "clustered" and the data as "clustered data". Examples of clustered data include repeated measures or hierarchical shared association (e.g., individuals within families). This paper provides an overview of the R package htestClust, a tool for the marginal analysis of such clustered data with potentially informative cluster and/or group sizes. Contained in htestClust are clustered data analogues to the following classical hypothesis tests: rank-sum, signed rank, \(t\)-, one-way ANOVA, F, Levene, Pearson/Spearman/Kendall correlation, proportion, goodness-of-fit, independence, and McNemar. Additional functions allow users to visualize and test for informative cluster size. This package has an easy-to-use interface mimicking that of classical hypothesis-testing functions in the R environment. Various features of this package are illustrated through simple examples.

Mary Gregg (Department of Bioinformatics and Biostatistics) , Somnath Datta (Department of Biostatistics) , Douglas Lorenz (Department of Bioinformatics and Biostatistics)
2022-10-11

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-024.zip

References

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Citation

For attribution, please cite this work as

Gregg, et al., "htestClust: A Package for Marginal Inference of Clustered Data Under Informative Cluster Size", The R Journal, 2022

BibTeX citation

@article{RJ-2022-024,
  author = {Gregg, Mary and Datta, Somnath and Lorenz, Douglas},
  title = {htestClust: A Package for Marginal Inference of Clustered Data Under Informative Cluster Size},
  journal = {The R Journal},
  year = {2022},
  note = {https://doi.org/10.32614/RJ-2022-024},
  doi = {10.32614/RJ-2022-024},
  volume = {14},
  issue = {2},
  issn = {2073-4859},
  pages = {54-66}
}