[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Daniel Waxman" <dan@amplecat.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: how to deal with censoring at zero (a lot of zeroes) for a laboratory result which I would like to log transform |

Date |
Sun, 5 Jun 2005 10:10:57 -0400 |

Svend, Thanks. I did indeed look extensively as the predictor as a categorical variable and as a predictor when .005 is used. My dataset is large enough, and events common enough, that the confidence intervals are quite small at the .01 level. There is a threshold, but it is below .01. In other words, there is no measurable change in outcome between .01 and .02, but there is one between 'undetectable' and .01. Zero could be .005, but it could be .0005 or .00005. (biologically speaking as well) I suppose this becomes irrelevant very soon though if it can't be measured. However, the logistic equation suggests (given the measured # of deaths at the zero value) that the zero should be approximately .001. It seems that this is a common issue in the environmental literature, where people care a lot about very small concentrations of things (lead, arsenic, etc.) I have found various sources that suggest that the method of Cohen (mentioned below) of estimating the entire distribution curve by using the available points and the known or assumed shape can be preferable to picking half of the lower limit arbitrarily. Daniel -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Svend Juul Sent: Sunday, June 05, 2005 9:47 AM To: statalist@hsphsun2.harvard.edu Subject: RE: st: how to deal with censoring at zero (a lot of zeroes) for a laboratory result which I would like to log transform Daniel, You wonder how to handle zero values in a predictor you have good reasons to log-transform. For a first look I would make a reasonable categorization of the predictor, e.g. five categories (0, 0.01-0.09, 0.10-0.99, 1-10, 10+) and use -xi: logistic- to see the pattern. This analysis might also give an idea whether there is some threshold. If this justifies using a log-transform, I think you almost give the answer yourself: zero means a result somewhere between 0 and 0.01. So why not select 0.005, log-transform, and run -logistic- using the log-transformed predictor. The idea to let the data determine the "best" value that the zeros represent has its problems: The confidence interval for the odds ratio estimate becomes too small. Hope this helps Svend * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: how to deal with censoring at zero (a lot of zeroes) fora laboratory result which I would like to log transform***From:*Dr Murray Finkelstein <murray.finkelstein@utoronto.ca>

**References**:**RE: st: how to deal with censoring at zero (a lot of zeroes) for a laboratory result which I would like to log transform***From:*"Svend Juul" <SJ@SOCI.AU.DK>

- Prev by Date:
**RE st: Automatically changing -ylabel()- values using -graph-** - Next by Date:
**st: RE: how to deal with censoring at zero (a lot of zeroes) for a laboratory result which I would like to log transform** - Previous by thread:
**RE: st: how to deal with censoring at zero (a lot of zeroes) for a laboratory result which I would like to log transform** - Next by thread:
**Re: st: how to deal with censoring at zero (a lot of zeroes) fora laboratory result which I would like to log transform** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |