clinsig {clinsig}R Documentation

Calculate clinical significance criteria.

Description

Calculates one or more parametric or non-parametric criteria for clinical significance.

Usage

 clinsig(pre.x,post.x,dys.mct=NA,func.mct=NA,dys.disp=NA,func.disp=NA,
 mct="mean",disp="sd",disp.mult=c(2,2),dys.qnts=NA,
 dir.effect=NA,xlim=range(c(pre.x,post.x),na.rm=TRUE),
 ylim=range(c(pre.x,post.x),na.rm=TRUE),pch=par("pch"),
 do.plot=TRUE,point.id=NA,...)

Arguments

pre.x A vector of pre-intervention assessment scores.
post.x A vector of post-intervention assessment scores for the same measure as pre.x.
dys.mct A Measure of Central Tendency (MCT) for the scores of the population of dysfunctional persons on the measure used. If absent, it will be estimated from pre.x.
func.mct A Measure of Central Tendency for the normative ("functional") scores on the measure.
dys.disp A measure of DISPersion for the scores of the population of dysfunctional persons on the measure. If missing, it will also be estimated.
func.disp A measure of DISPersion for the normative scores on the measure.
mct The name of a Measure of Central Tendency to be used. Defaults to the mean.
disp The name of a measure of DISPersion to be used. Defaults to the standard deviation.
disp.mult The multiple(s) of the dispersion measure to be used in calculations. If different multiples are to be used for the pre/post measures and the normative scores, pass a two element vector containing the multipliers for the pre/post and then the normative dispersions. Defaults to 2 for both.
dys.qnts The lower and upper quantiles of the pre-assessment intervention scores to be used in calculating the "a" criterion. If a two element vector of quantiles such as c(0.16,0.84) is passed, this will be used instead of dys.disp or the dispersion calculated for pre.x. See Details.
dir.effect The direction of effect expected of the intervention. This argument should only take the values of -1 or 1. see Details.
xlim,ylim Allows the user to set the x and y axes to the range desired. Defaults to range(pre.x) and range(post.x) respectively.
pch Symbols to use in the plot.
do.plot Whether to display a significance plot.
point.id Optional text to be displayed instead of symbols for each score pair.
... Additional arguments passed to plot.

Details

The clinsig function calculates both parametric and non-parametric versions of the Jacobson-Truax estimates of clinical significance. The number of estimates produced will depend upon which arguments are supplied. The "a" estimate is always calculated as it only requires the measure of central tendency and dispersion of the pre-intervention scores. The c estimate requires that the normal (non-pathologic) measure of central tendency be specified. The "b" estimate requires the dispersion of the normal MCT. If the dispersion of the dysfunctional scores is not supplied, the function will estimate this with the function named in disp. The user can calculate a measure of dispersion and pass it as dys.disp or write a function and pass the name of that function as disp.

Using the "a" cutoff means that post-intervention scores are significant if they are at least a specified distance from the MCT of pre-intervention scores in the direction of the normal MCT (i.e. they are "far enough" from the dysfunctional MCT). The "b" cutoff means that post-intervention scores are significant if they are within a specified distance of the normal MCT (i.e. they are "close enough" to the normal MCT). The "c" cutoff means that significant post-intervention scores are in the direction of the normal MCT and beyond the weighted mean of the two MCTs (i.e. they are "on the normal side" of the average of the MCTs). Look at the second plot in the examples for an illustration of this in which there is a large effect and a clear separation of pre- and post- scores.

The direction of effect is usually calculated as the sign of the functional (normal) MCT minus the dysfunctional MCT. If one or both of these are missing, the pre- and post-MCTs are substituted. This assumes that the post-MCT is in the expected direction toward the functional MCT. If this is not the case, the user should set dir.effect to 1 (higher scores = improvement) or -1 or the function may report improvement when the scores are actually indicating deterioration.

If only the pre-intervention and post-intervention scores are provided, the function calculates a cutoff score for significant change based upon disp.mult times the measure of disperson for the pre-intervention scores if dys.disp is specified. However, if dys.qnts is not NA, the "a" cutoff score is set to one of the quantiles of the pre-intervention score distribution. If the normal MCT is supplied and it is less than the pre-intervention MCT, or dir.effect is -1, the lower quantile is used. In all other cases, the upper cutoff is calculated. Remember that the quantile is not adjusted for disp.mult.

Value

A list containing:

pre.x the pre-assessment scores
post.x the post-assessment scores
crit the three estimated criteria, some of which may be NA
sigsums the number of scores that met each of the three criteria
pre.mct the pre-intervention MCT
post.mct the post-intervention MCT
func.mct the normative MCT
mct the name of the function used to calculate the MCTs
disp the name of the function used to calculate the dispersions
post.n the number of post-intervention assessments
passed a vector of three logicals indicating whether the post-intervention MCT met the respective criteria


As a side effect, a scatterplot of the pre- and post-scores and MCTs with the calculated cutoff scores as lines is displayed.

Author(s)

Jim Lemon

References

Jacobson, N.S. & Truax, P. (1991) Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59(1): 12-19.

Examples

 pre.x<-runif(30,3,6)
 post.x<-runif(30,1,4)
 clinsig(pre.x,post.x,func.mct=1,func.disp=1,xlim=c(1,6),ylim=c(1,6))
 # simulate scores on a typical psychological assessment with a limited
 # range and a large separation between the pre- and post- assessments
 pre.x<-c(3,3,4,5,5,6,6,6,6,7,7,7,8,8,8,8,9,9,10,10)
 post.x<-c(13,12,15,14,12,18,13,17,NA,20,16,22,23,15,19,17,18,21,13,15)
 big.sep<-clinsig(pre.x,post.x,mct="median",disp="mad",func.mct=19,func.disp=2,
  do.plot=FALSE)
 hist(big.sep,main="Widely separated samples")
 legend(20,3.8,c("Pre","Post"),fill=c("red","green"))
 # now squeeze the two samples together to show how the criteria change positions
 post.x<-post.x-7
 little.sep<-clinsig(pre.x,post.x,mct="median",disp="mad",func.mct=15,func.disp=2,
  do.plot=FALSE)
 hist(little.sep,main="Closely spaced samples")
 legend(12.5,3.8,c("Pre","Post"),fill=c("red","green"))

[Package clinsig version 1.0-1 Index]