Hi Daniel,
It looks to me like you could use -tobit- for log(tropin) and just a
constant. The predicted values should give you the extrapolations you
want. (This will be the same value for all missing observations: the
mean of the log-normal distribution conditional on being less than the
censoring value)
However, These are actually missing values, and apperently you want to
create imputations for them. If you just use the values you obtained
from -predict- you will be assuming that you are as sure about these
values as you are about the values you actually observed, and thus get
standard errors that are too small. If you really want to impute, than
you could have a look at -mice- (findit mice). Alternatively, you
could use the results from -tobit- to generate multiple imputations.
Mail me if you want to do that, and I can write, tonight or tomorrow,
an example for the infamous auto dataset. However, censoring on the
independent variable is generally much less a problem than censoring
on the dependent variable, so ignoring (throwing away) the censored
observation, should not lead to very different estimates.
HTH,
Maarten
--- "Daniel Waxman" <dan@a...> wrote:
> I am modeling a laboratory test (Troponin I) as an independent
> (continuous) predictor of in-hospital mortality in a sample of
> 10,000 subjects. <snip> The problem is the zero values, what they
> represent, and what to do with them. The distribution of results
> ranges from the minimal detectable level of .01 mcg/L to 94 mcg/L,
> with results markedly skewed to the left (nearly half the results
> are zero; 90% are < .20. results are given in increments
> of .01). Of course, zero is a censored value which represents a
> distribution of results between zero and somewhere below .01.
<snip.
> I found a method attributed to A.C. Cohen of doing essentially this
> which uses a lookup table to calculate the mean and standard
> deviation of an assumed log-normal distribution based upon the
> non-censored data and the proportion of data points that are
> censored, but there must be a better way to do this in Stata.
>
> Any thoughts on (1) whether it is reasonable to assume the
> log-normal distribution (I've played with qlognorm and plognorm, but
> it's hard to know what is good enough), and if so (2) how to do it?
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