README.md

In AWKruijt/JT-RCI: obtain and plot Jacobson-Truax and reliable change indices

JTRCI: Jacobson-Truax & reliable change indices

Example plot using the Dyadic Adjustment Scale (DAS) data provided in table 2 by Jacobson & Truax (1991).

notice that this is a ‘higher is better’ measure

to install JTRCI, first install package devtools from CRAN - either through the ‘install packages’ interface (in for instance Rstudio) or using the following line:

install.packages("devtools")

then install JTRCI from github:

devtools::install_github("AWKruijt/JT-RCI")

library(JTRCI)

JRTCI() is the main function in this package:

JTRCI (data = NA, pre = NA, post = NA, ppid = NA, group = NA,                reliability = NA, higherIsBetter = F, indextype = “JT”, JTcrit = “auto”,                normM = NA, normSD = NA, dysfM = NA, dysfSD = NA,                plot = T, table = T, …)

It requires data in wide format (one row per individual) and an estimate of the measure reliability to be given through parameter ‘reliability =’.

The reliability estimate can be based on norm data. Alternatively, the internal reliability of the measure observed in the current data can be used. In that case, the user will first need to obtain their estimate of choice (alpha, omega, x-random splits splithalf reliability) using the raw (item level) data.

Obtaining reliable change indices is pretty straightforward: run the function with parameter ‘indextype = “RCI”’. The JTRCI function’s default setting is to obtain Jacobson-Truax indices (‘indextype = “JT”’). The function is designed to provide guidance through the various choices a researcher has to make when applying the Jacobson-Truax method. It is recommended to pay attention to the various output messages.

When plot = T (default), the JTRCI() function will call on either the JTplot() or RCIplot().

The JTplot()/RCIplot() functions take the following parameters:

plotJT(data = JTRCIdf, useGroups = F, facetplot = F, addJitter = F,              xlab = “pre”, ylab = “post”, plottitle = “Jacobson-Truax plot”,              addInfoLegend = c(“yes”, “classcounts”, “JTcrit”, “no”))

The plot parameters can also be passed directly into a call on JTRCI() (see examples below)

An online dashboard using essentially the same code is available at: https://awkruijt.shinyapps.io/JTRCI_dashboard/ It allows the user to upload their own data (as a .csv file) or to test things using generated/mock data. JTRCI plots and dataframes can subsequently be downloaded.

An example of a more advanced plot (with multiple follow-ups) based on the JTRCI-outputs can be found here: https://awkruijt.netlify.com/plotposts/longitudinaljtrci

examples:

# generate some random data:
df <- cbind.data.frame("ppid" = seq(1:64), 
                       "pre" = rnorm(64, 65, 8), 
                       "post" = c(rnorm(32, 40, 8), rnorm(32, 45, 8)),
                       "group" = rep(c("treatment", "control"), each = 32))

Obtain and plot Jacobson-Truax indices using parameter JTcrit = “auto”

# The function will determine which criterion to use based on the available information 
# f.i. when no (healthy or dysfunctional) norm values are provided, the function will  
# return criterion A using the baseline distribution as the 'dysfunctional distribution':

JTRCI(data = df, 
      ppid = "ppid", 
      pre = "pre", 
      post = "post",  
      reliability = .8, 
      indextype = "JT", 
      JTcrit = "auto")

## Assumed that lower scores are better (and reduction == improvement),
##  if that is incorrect: set higherIsBetter = T

## NB: using the sample baseline distribution to characterize the dysfunctional population. 
##     to change: provide norms for dysfunctional population using 'dysfM =' and 'dysfSD ='

## Jacobson-Truax criterion A: 47.5

##  this value represents two sd from the baseline  sample mean

## 1 participants scored below the Jacobson-Truax cut-off score at the pre-measurement 
##  interpret their Jacobson-Truax classification with caution

##    Jacobson-Truax classification  N
## 1:                  deteriorated  0
## 2:                     unchanged  4
## 3:                      improved 10
## 4:        non reliably recovered  5
## 5:                     recovered 45

Obtain reliable change indices by setting parameter indextype = “RCI”:

# obtain reliable change indices 
# disable table and plot with 'table = F' & 'plot = F'
JTRCI(data = df, pre = "pre", post = "post", group = "group", ppid = "ppid",
      reliability = .8, indextype = "RCI",  table = F, plot = F)

## Assumed that lower scores are better (and reduction == improvement),
##  if that is incorrect: set higherIsBetter = T

Subsequently plot the RCI with a separate call to plot_RCI():

# plot the reliable change indices obtained in the previous chunk - set x y and plot labels:

plotRCI(xlab = "score pre", ylab = "score post", plottitle = "my reliable change plot", useGroups = F)

and obtain RCI tables by calling tableRCI():

tableRCI()

##    reliable change classification  N
## 1:          reliably deteriorated  0
## 2:             no reliable change  9
## 3:              reliably improved 55

tableRCI(useGroups = T)

##    reliable change classification control treatment
## 1:          reliably deteriorated       0         0
## 2:             no reliable change       6         3
## 3:              reliably improved      26        29

Parameters for the plot_JT()/plot_RCI() functions can also be passed directly in the JTRCI() function:

# obtain and plot Jacobson-Truax indices using criterion B 
# Crit B requires functional/healthy norm data passed as 'normM =' and 'normSD='. 
# set 'useGroups = T' to show group membership in the plot - JTplot() can handle up to 5 groups
# add jittering of the points with addJitter = T
# pass a custom plottitle with parameter 'plottitle = '

JTRCI(data = df, pre = "pre", post = "post",  group = "group", ppid = "ppid",
      reliability = .8, indextype = "JT", JTcrit = "B",
      normM = 30, normSD = 5, 
      useGroups = T, addJitter = T, plottitle = "JT indices (crit B):")

## Assumed that lower scores are better (and reduction == improvement),
##  if that is incorrect: set higherIsBetter = T

## NB: using the sample baseline distribution to characterize the dysfunctional population. 
##     to change: provide norms for dysfunctional population using 'dysfM =' and 'dysfSD ='

## NB criterion C is recommended when the baseline distribution overlaps with the norm distribution

## Jacobson-Truax criterion B: 40

##  this value represents two sd from the functional/healthy population norm mean

##    Jacobson-Truax classification control treatment
## 1:                  deteriorated       0         0
## 2:                     unchanged       6         3
## 3:                      improved      22        14
## 4:        non reliably recovered       0         0
## 5:                     recovered       4        15

Plot groups in separate facets:

# obtain Jacobson-Truax indices using criterion C
# Crit C requires both 'functional' and 'dysfunctional' norm data: 
# these are passed as 'normM =', 'normSD=', 'dysfM =' and 'dysfSD ='. 
# plot with parameter facetplot = T 

JTRCI(data = df, pre = "pre", post = "post",  group = "group", ppid = "ppid",
      reliability = .8, indextype = "JT", JTcrit = "C",
       normM = 30, normSD = 5, dysfM = 60, dysfSD = 5, 
      facetplot = T, plottitle = "JT indices by group:")

## Assumed that lower scores are better (and reduction == improvement),
##  if that is incorrect: set higherIsBetter = T

## Jacobson-Truax criterion C:42.8

##  this value represents the weighted midpoint between the dysfunctional and functional norm mean, 
##  i.e. the value at which an individual is equally likely to belong to the functional as to the dysfunctional population

##    Jacobson-Truax classification control treatment
## 1:                  deteriorated       0         0
## 2:                     unchanged       4         3
## 3:                      improved      15        11
## 4:        non reliably recovered       2         0
## 5:                     recovered      11        18

It is important to note that when assessing/plotting Jacobson-Truax indices for multiple groups, the function treats the groups as one group for the calculation of the RCI (when using baseline characteristics as the ’dysfunctional distribution), or uses one single set of inputs dysfM and dysfSD as characteristic of the dysfunctional population. I.e. it assumes that all participants originate from the same (sub)population and were randomly assigned to the different groups/conditions.

Generally speaking (regardless of number of groups), Jacobson-Truax indices are designed for use with participants selected so that they would be expected to score high (or low in case of a higher-is-better measure) on the measure assessed. For a control group in which most/all participants score already within the ‘functional’ (norm) range the traditional Jacobson-Truax classification labels make little sense: i.e. someone who scored in the healthy range at both the pre- and the post-assessment would be classified as ‘recovered’ without an actual recovering happening. The JTRCI() function will issue a message if any participant scored below the JT criterion at baseline.

If such cases occur and are problematic, the more ‘simple’ reliable change indices can be used (set parameter ‘indextype = “RCI”’). Reliable change indices can also be used for comparison of groups preselected to (likely) differ at baseline on the measure assessed.

I’ve also taken the liberty of adding a new label to the Jacobson-Truax classification to indicate a sequence of events seen in the data that this code was originally developed for: participants who dropped below the JT-criterion at post, but did not actually show a pre-post change sufficiently large to be classified as reliable change (i.e. they scored already close to the JT-criterion value at basline). Such participants are labeled ‘non-reliably recovered’.

AWKruijt/JT-RCI documentation built on July 9, 2019, 3:54 p.m.