From: Chris Evans <chrishold_at_psyctc.org>

Date: Mon 07 Aug 2006 - 22:00:07 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon Aug 07 22:04:49 2006

Date: Mon 07 Aug 2006 - 22:00:07 EST

Eleni Rapsomaniki sent the following at 07/08/2006 11:35:

> Dear mailing list,

*>
**> For two normal distributions, e.g:
**>
**> r1 =rnorm(20,5.2,2.1)
**> r2 =rnorm(20,4.2,1.1)
**> plot(density(r2), col="blue")
**> lines(density(r1), col="red")
**>
**> Is there a way in R to compute/estimate the point(s) x where the density of the
**> two distributions cross (ie where x has equal probability of belonging to
**> either of the two distributions)?
*

I worry about showing my statistical incompetence or incomprehension but isn't what you need Jacobson et al.'s criterion C for clinical change? I.e. the point at which the misclassification rates in two Normal distributions, one with a higher mean than the other, match.

It's at (sd1*mean2 + sd2*mean1)/(sd1 + sd2)

So for Eleni's example I think that comes out at 4.544 and if I use:

*> r1b <- rnorm(200,5.2,2.1)
**> r2b <- rnorm(200,4.2,1.1)
**> plot(density(r2b), col="blue")
**> plot(density(r1b), col="red")
**> plot(density(r2b), col="blue")
**> lines(density(r1b), col="red")
*

> cscc <- 4.544

> abline(v=cscc)

It happened to work out beautifully:

> sum(r1b > cscc)

[1] 126

> sum(r2b < cscc)

[1] 126

of course, set a different seed (I broke the posting rules and didn't set one, yes, I know) you won't get such a nice result every time and with n=20 in each group you'll get much more wobble.

Or am I missing something. The original paper, which got reliable change wrong, was:

Jacobson, N. S., Follette, W. C. & Revenstorf, D. (1984) Psychotherapy outcome research: methods for reporting variability and evaluating clinical significance. Behavior Therapy, 15, 336-352.

There's a summary most people cite at:

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, 12-19.

and shameless self-promotion here, I tried to summarise it: Evans, C., Margison, F. & Barkham, M. (1998) The contribution of reliable and clinically significant change methods to evidence-based mental health. Evidence Based Mental Health, 1, 70-72.

I hadn't twigged that what the criterion gives is balanced missclassification when I wrote that. I've played with some simulations and it's not as vulnerable to non-Gaussian distributions as I'd expected but someone can probably point to published work, simulation or analytic, on that.

Cheers all,

Chris

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