Date: Sun, 6 Jun 2004 10:16:23 +0100
From: Edith Reagan <[log in to unmask]>
Subject: Data Analysis and Reduction
I would grateful for some advice. I have inherited a project in which the
dataset consists of 52 variables and 780 results. The variables are clinical
indicators, and are a mixture of binary and ordinal responses.
There are no missing values. The dataset covers 7 tumour types. The object
is to select those variables that "best" characterise each type and then use
these to develop a "scale" to predict tumour type. I have considered cluster
or factor analysis and even discriminant analysis based on random sampling
from the dataset. Unfortunatley my stats degree did not include a real world
problem such as this. Can anybody recommend any particular technique or
suitable references? I have been using Everitt as my prime reference.
Many thanks,
Edith
Dear Edith,
seems to be a more complicated classification problem since you want to
calculate a classification rule based on a set of clinical parameters. Are
you interested in professional statistical consulting or data analysis
services? We are experts in analysis of data from clinical oncology,
prognostic classification schemes etc. Please see our website for further
informations.
Kind Regards
Bernd
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Dr Bernd Genser
MSc, PhD
General Manager
BGStats Consulting
Statistical Consulting - Data Analysis - Medical Research Support
email: [log in to unmask]
www.bgstats.com
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