Re: st: Nonparametric Methods for Longitudinal Data

["Roger B. Newson" <r.newson@imperial.ac.uk>]

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
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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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