pchc: Calculate the Paretian Classification of Health Change

In fragla/eq5d: Methods for Analysing 'EQ-5D' Data and Calculating 'EQ-5D' Index Scores

Calculate the Paretian Classification of Health Change

Description

Calculate the Paretian Classification of Health Change (PCHC) for two EQ-5D datasets.

Usage

pchc(
  pre,
  post,
  version = NULL,
  no.problems = TRUE,
  totals = TRUE,
  by.dimension = FALSE,
  ignore.invalid = TRUE,
  dimensions = .get_dimension_names(),
  summary = TRUE
)

Arguments

pre	data.frame, numeric or character. For data.frame default column names should be MO, SC, UA, PD and AD representing Mobility, Self-care, Usual activities, Pain/discomfort and Anxiety/depression. Vector using five digit format can also be used.
post	data.frame, numeric or character. For data.frame default column names should be MO, SC, UA, PD and AD representing Mobility, Self-care, Usual activities, Pain/discomfort and Anxiety/depression. Vector using five digit format can also be used.
version	string of value "3L" or "5L" to indicate instrument version.
no.problems	boolean. Summarise 11111 "No change" subjects in a "No problems" group.
totals	boolean. Include a summary total.
by.dimension	boolean. Summarise results by each EQ-5D dimension rather than by the whole dataset.
ignore.invalid	boolean whether to ignore invalid scores. TRUE returns NA, FALSE throws an error.
dimensions	character vector, specifying "dimension" column names. Defaults are "MO", "SC", "UA", "PD" and "AD".
summary	boolean. Summarise results or return all classifications.

Value

a data.frame or list of data.frames of changes according to PCHC. contain dimensions names and rows the EQ-5D score or, if summary=FALSE, a vector or list of vectors of changes.

Examples

dat <- read.csv(system.file("extdata", "eq5d3l_example.csv", package="eq5d"))

pre <- dat[dat$Group=="Group1",][1:50,]
post <- dat[dat$Group=="Group2",][1:50,]

pchc(pre, post, version="3L", no.problems=FALSE, totals=FALSE)

fragla/eq5d documentation built on Nov. 27, 2024, 3:23 a.m.