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From |
"Rosy Reynolds" <rr@dandr.demon.co.uk> |

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

Subject |
st: estimating measurement error with interval censoring |

Date |
Thu, 7 Feb 2008 10:07:03 -0000 |

Hi,

I am working with a rather wobbly measurement method where the readings are also interval-censored. (It is a doubling-dilutions titration, for anyone interested. After transforming to a log2 scale, the measurements appear as integers where a reading of e.g. x=4 actually means 3<x<=4.)

I am interested in characterising the repeatability of the measurements, and have some replicates to work with. I have about 300 samples, each of which has been measured on up to 8 occasions, typically 6-7 occasions, but sometimes only once or twice. This has some similarities with panel data with N around 300, T around 6. The actual measurement will be different in each of the 300 samples, but I'm not interested in that. I am only interested in the variation between replicates, and I am prepared to assume that the variation is normally distributed and the same for all samples.

My idea so far is to use -intreg- and include dummies for sample. I was concerned about using dummies, and such a large number of them, having been warned frequently about the dangers of group dummies with logistic regression, but I thought it should be OK as this is linear regression (see e.g. the footnote in this post from Bill Gould http://www.hsph.harvard.edu/cgi-bin/lwgate/STATALIST/archives/statalist.0710/date/article-934.html). However, a very quick simulation suggests that the estimate of the variation (obtained from e(sigma) following -intreg-) is biased downwards when the number of replicates per sample is small-ish.

I'd be very grateful if someone on the list with experience in this sort of thing could comment about whether the -intreg- plus dummies approach is reasonable, or suggest something more appropriate.

Thank you.

Rosy Reynolds

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