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Re: st: Nonparametric Methods for Longitudinal Data


From   "Roger B. Newson" <r.newson@imperial.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Nonparametric Methods for Longitudinal Data
Date   Mon, 11 Feb 2013 13:09:10 +0000

Thomas might like to investigate the -somersd- package, which can be downloaded from SSC. The -somersd- package computes rank statistics (with confidence limits) for ordinal data, which may be clustered and/or sampling-probability weighted.

I hope this helps.

Best wishes

Roger

Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/

Opinions expressed are those of the author, not of the institution.

On 11/02/2013 12:34, Nick Cox wrote:
Questions like this raise more questions in their wake.

It is a bit puzzling that you have apparently only just discovered how
your response variable is defined. However, many medical and
psychiatric analyses make use of scores usually devised according to
the answers to multiple questions. They often work at least
approximately like measured variables; many researchers would argue
that treating them as ordinal is too pessimistic and indeed there are
usually too many distinct values for many standard models for ordinal
responses to work well.

IQ is an example familar to many.

Statistically, it's a myth on several levels that "parametric
analysis" requires a response variable to be normally distributed. At
most, it's a secondary assumption of some regression-like methods that
error disturbances be normally distributed. There are also many
methods that are not non-parametric for other distributions
(exponential, gamma, etc., etc.).  Also, what about transformations or
similar link functions.

So, manifestly I can't see your data but I'd suggest that your
impression that you need quite different methods is jumping to
conclusions prematurely.

"Stata" is so spelled.

Nick

On Mon, Feb 11, 2013 at 12:16 PM, Thomas Herold <thomasherold@gmx.net> wrote:

I am currently analysing a dataset on the influence of certain treatments on
depression. We have three different treatment groups and five points of
measurement.

The problem is that it has recently been discovered that the depression
score we are working with can only be interpreted as ordinal data. What´s
more, the resulting variable is far from being normally distributed - the
data is just not suitable for parametric analysis.

Concerning the independent variables: Some of them change over time (e.g.
financial situation), others are time-invariant (e.g. treatment).

Is there any nonparametric model for longitudinal data in STATA? Does anyone
have any reading tips?

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