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Re: st: RE: Interval regression with skewed data

Subject   Re: st: RE: Interval regression with skewed data
Date   Tue, 10 Jan 2012 08:40:03 +0000

Hello Nick, Alan,

Thank you both for your replies.

Nick, I apologise for not being clear in my original posting.  My 
outcome/dependent variable is the number of colony forming units per ml, 
and my predictor/independent variable is the region (North West, North 
East, South East England,...) within which the sample was taken.  I 
gravitated towards interval regression because I have some observations 
that are left censored and some that are right censored but the censoring 
value is not always the same, and I started to think about survival 
analysis because I had seen a suggestion where this could be used to 
perform interval regression when the Normality assumption was violated. 
Unfortunately, my outcome has some zero counts and so I cannot really use 
the logarithmic transform.  I am more than happy to consider other methods 
of analysis if you have any ideas?

Alan, I am afraid that what you suggest is probably outside of my 
programming and statistical expertise, and would also take me longer than 
the time I have to look at this problem.

Many thanks,


From:   Nick Cox <>
To:     "''" 
Date:   09/01/2012 16:15
Subject:        st: RE: Interval regression with skewed data
Sent by:

I'd be more worried about violating linearity of functional form than 
normality of errors, but you say nothing about that. Nor do you say 
anything about what your predictors are. 

I can't see from your discussion that it can be a choice between interval 
regression and some kind of survival analysis. What you have doesn't sound 
to me at all like a survival analysis problem. 

However, assuming the first, you could transform before you use -intreg-. 
Your limits just transform to limits on your transformed scale. From other 
experiences with hydrological data I would reach for a logarithmic 
transform as first port of call. You would need to back-transform 


I am struggling with an analysis and would like your insight.  I think 
that I am looking at using interval regression but there are certain 
aspects of the data that are worrying me.  First some background...

A number of water samples have been taken from around the UK, and a 
microbiological examination of the water has been undertaken.  Whenever a 
sample is sent to a lab, a whole suite of tests are done to count the 
number of colony forming units of various organisms.  I therefore have a 
number of outcomes, whose units are the number of colony forming units per 

ml.  The aim of this part of the analysis is to compare the organism 
levels found in different regions of the UK.

Some observations are left censored (0-6% depending on the outcome) - ie 
<1 CFU/ml, or <10 CFU/ml - and some are right censored (0-59%) - ie. >3000 

CFU/ml.  The censoring point varies,and so I thought that I would have to 
use interval regression (Stata's -intreg-).

However, the data are not Normally distributed (which is an assumption of 
interval regression), but are positively skewed with some outcomes having 
a high number of zero counts (one has 75% zeros!).  In the book by J S 
Long (Regression models for categorical and limited dependent variables, 
2007), there was a discussion about how accelerated failure time (AFT) 
models can be used to perform interval regression when the data are not 
Normally distributed, but there was no example of how to do this. 
Unfortunately I no longer have the book to provide you with the page 

I have found a user written command -intcens-, which can perform 
interval-censored survival analysis and fits a number of different 
distributions, but I cannot find any documentation or examples of its use 
(apart from the help file).

Does anyone have any examples of using AFT models to perform interval 
regression or examples of using -intcens-?  Or do you think that there is 
a better way I could be handling the data?

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