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A 12‐week longitudinal study with 247 US menopausal women was undertaken to investigate the perceptual efficacy of a tablet formulated with an extract of Siberian rhubarb to provide relief from menopausal symptoms. The 5‐point menopause rating scale (MRS) (0 = none; 1 = mild; 2 = moderate; 3 = severe; 4 = extremely severe) was used to measure symptom intensity. The MRS scale, an ordinal categorical scale, was transformed into an R‐index, a ratio scale, using the frequencies of the MRS categories. Symptom relief was compared to an initial week baseline measure; defined as R‐index = 0.5. An index, A.RDEX, which is based on area under the R‐index curve (AUC) in the range of 0–84 days was used to measure percentage efficacy of symptom relief. Results show that symptom relief occurs through the first 17 days in terms of R‐index = 0.64 for the total score and improves with continued use, reaching an A.RDEX = 0.42 by 84 days, suggesting about 42% relief considering all 11 MRS attributes. Practical Applications: MRS ratings data can be transformed into an R‐index using MRS category frequencies at an initial week and a subsequent week. Functional data analysis for R‐index values provides an advanced statistical model for analysis of perceptual differences throughout longitudinal studies that can examine MRS attribute effects over time. R codes are developed and provided in the paper to transform MRS data into R‐index measures and to conduct functional data analysis for R‐index measures obtained from different time points (weeks or days). [ABSTRACT FROM AUTHOR]

The sparse hierarchical clustering (SHC) method and the R package "sparcl" are used for consumer segmentation with 247 perimenopausal and postmenopausal US women. The data includes 11 menopause rating scale (MRS) attributes measuring severity of symptoms collected in the first week of a 12‐week extended use menopause tablet perceptual efficacy study. SHC is one of the advanced statistical clustering approaches for high‐dimensional data with potential noise variables. Sparse clustering is an important topic of the actively developing field of statistical learning with sparsity. An advantage of the SHC method is that it simultaneously finds the clusters of subjects and the important clustering variables. It allows clustering the data in low‐dimensional subspaces without dimension reduction of the data before clustering. The SHC method is particularly applicable to consumer segmentation based on high‐dimensional consumer data. Practical Applications: Sparse hierarchical clustering (SHC) provides advantages in consumer segmentation based on high‐dimensional consumer data with potential noise variables. This advanced clustering method finds the relative importance of attributes in clustering, helping to identify homogeneous clusters and enhance understanding of how consumer responses/needs vary among clusters. [ABSTRACT FROM AUTHOR]