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From | "Seed, Paul" <paul.seed@kcl.ac.uk> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
Subject | st: Correlations for censored data |
Date | Wed, 23 Oct 2013 11:29:05 +0000 |
Dear Statalist, I have data for a group of subjects on a large number of biomarkers that are sometimes measured, sometimes only recorded as "below the limit of detection", and sometimes even "above the limit of accuracy". Apart from the censoring, I anticipate that the values will be Normally distributed after log transformation. So the (transformed) data is censored multivariate Normal, with some underlying distribution _N_(_Mu_, _Sigma_), where _Mu_ is a vector of means, and _Sigma_ is a matrix of covariances. Examples: Subject Marker1 Marker2 Marker 3 1 <12 20 37 2 144 < 5 28 3 >3000 44 87 4 . . . 5 . . . I want to reduce the number of biomarkers via factor analysis. Is it possible to estimate the true (Pearson's product moment) correlation between each pair of biomarkers (i.e. what I would get if I had the actual values). I am hoping for something like the -tetrachoric- command; or at least some advice about how to handle the maximum likelihood calculations. Paul T Seed, Senior Lecturer in Medical Statistics, Division of Women's Health, King's College London Women's Health Academic Centre, King's Health Partners (+44) (0) 20 7188 3642. * * 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/