There is one caveat with what Ronan states
" This corresponds to the probability that a person
in a clinical trial, for instance, will have a better outcome with one
treatment rather than with the other. "
To be really pernickety one should state
" This corresponds to the probability that a person
in a clinical trial, for instance, will be observed to have a
better outcome with one treatment rather than with the other. "
One has to be very careful in interpreting overlap measures in
clinical trials because the same constant causal shift (say a 10mm HG reduction
in diastolic blood pressure) for every patient on active treatment would
translate into a different degree of overlap depending not only on the variability
of true values in the population but also on within-subject error, including
measurement error.
See
U is for Unease: Reasons for Mistrusting Overlap Measures
for Reporting Clinical Trials
Statistics in Biopharmaceutical Research : 1–8.
Posted online on 20 Oct 2010.
Which is available free at http://pubs.amstat.org/toc/sbr/0/0
Stephen
Stephen Senn
Professor of Statistics
School of Mathematics and Statistics
Direct line: +44 (0)141 330 5141
Fax: +44 (0)141 330 4814
Private Webpage: http://www.senns.demon.co.uk/home.html
University of Glasgow
15 University Gardens
Glasgow G12 8QW
The University of Glasgow, charity number SC004401
From: Evidence based
health (EBH) [mailto:[log in to unmask]] On Behalf Of Ronan
Conroy
Sent: 24 January 2011 10:41
To: [log in to unmask]
Subject: Re: Concepts
On 23 Jan 2011, at 14:03, Cristian Baicus wrote:
NONparametric techniques are based on
ranks/signs, because mean and standard deviation have no sense (they
don’t describe the population, because the distribution is not
normal=Gaussian).
This is a misconception. So-called nonparametric techniques
often estimate useful useful parameters. For example, the Mann-Whitney Wilcoxon
test estimates the probability that an observation in one group will be greater
than an observation in the other group (with an allowance for ties). This
corresponds to the probability that a person in a clinical trial, for instance,
will have a better outcome with one treatment rather than with the other.
Means and standard deviations can be used to describe any
distribution, so I do not understand how they can have no sense in describing,
say, a uniform distribution. The mean is a useful descriptor of any
distribution (and it is the only descriptor necessary for a Poisson
distribution). And the standard deviation, while it can be calculated validly
on any distribution, has a limited role in communication, since most people
don't understand what it is – and this applies to normally distributed
data as well.
Ronán Conroy
[log in to unmask]
Associate Professor
Division of Population Health Sciences
Royal College of Surgeons in Ireland