hamlet (version 0.9.6)

orxwide: Wide-format baseline data for the ORX study

Description

This data frame contains the wide-format data of the ORX study for baseline characteristics of the individuals participating in the study. Some fields (Volume, PSA, High, BodyWeight, PSAChange) were used to construct the distance matrix in the original matching-based random allocation of individuals at baseline, while other variables (Group, Submatch) contain these results.

Usage

data("orxwide")

Format

A data frame with 109 observations on the following 8 variables.

ID

A unique character indicator for the different individual(s)

Group

After identifying suitable submatches, the data were distributed to blinded intervention groups. These groups were later then annotated to actual treatments or non-intervention control groups.

Submatch

Submatches identified at baseline using the methodology presented in this package

Volume

Tumor volume at baseline in cubic millimeters

PSA

Raw baseline PSA measurement values in unit (ug/l)

High

The highest dimension in the tumor in millimeters, giving insight into the shape of the tumor

BodyWeight

Body weight at baseline in unit (g)

PSAChange

A fold-change like change in PSA from the prior measurement defined as: (PSA_current - PSA_last)/(PSA_last)

Details

Originally, 3-fold weighting of the baseline 'Volume' and 'PSA' was used in comparison to 'High', 'BodyWeight' and 'PSAChange' when computing the distance matrix. Furthermore, some individuals were annotated prior to matching for exclusion based on outlierish behaviour. The exclusion criteria were applied before any interventions were given or the matching was performed. The excluded tumors had either non-existant PSA, non-detectable tumor volume, or extremely large tumors (volume above 700 mm^3).

Examples

# NOT RUN {
data(orxwide)
# Construct an example distance matrix based on conventional 
# Euclidean distance and the baseline characteristics
d.orx <- dist(orxwide[,c("Volume", "PSA", "High", "BodyWeight", "PSAChange")])
# Plot a hierarchical clustering of the individuals
plot(hclust(d=d.orx))
# This 'd.orx' may then be further processed by downstream experiment 
# design functions such as match.ga, match.bb, etc.
# }