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| 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 |
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?* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/
* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/