Bill- Hidden markov models (HMMs) might provide a way to solve this. Browse Steve Scott's work as he's published a couple of papers using HMMs in the context of pharmaceutical clinical trials. Best regards, Tom Ball
William Shannon <[EMAIL PROTECTED]> wrote: >I consider this a clustering problem (aren't all problems clustering >problems?). I have been trying to find a solution but can't find anything >more sophisticated than pairwise t-tests which is less than optimal. > >The problem we are attacking is the following. In cancer epidemiology >survival curves are estimated for different strata (i.e., different curves for >different tumor types by tumor grade by gender by age category, etc.). Rob >Culverhouse and I have been publishing work on non-linear modeling in genetics >and want to apply it to the analysis of this type of cancer survival data. > >We are starting with lung cancer data (several 10's of thousands of records) >with survival time/censoring as well as four tumor types (e.g., adenocacinoma, >small cell) and 5 tumor grades (grades I - IV and unknown) giving us a 4 x 5 >table. Within each cell is a survival curve. > >We would like to collapse these 20 cells into a smaller number such that cells >collapsed together have equal survival functions. > >Ideally I would like an analogous method to multiple comparisons in post-hoc >anova or G^2 statistic (?) in log-linear modeling. > >Any hints would be appreciated. > >Thanks >Bill > > > > > >---------------------------------------------- >CLASS-L list. >Instructions: http://www.classification-society.org/csna/lists.html#class-l > > ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
