Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

st: delta method for unobserved confounding coefficient

From   Adam Olszewski <>
Subject   st: delta method for unobserved confounding coefficient
Date   Sun, 24 Feb 2013 14:28:13 -0500

Dear listers,
I was wondering if someone could enlighten me with regards to my
understanding of the delta method application in Stata.
I am trying to obtain sensitivity measures for unmeasured confounding
after Cox regression in IPT-weighted data. I would like to implement
the method from
Lin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression
results to unmeasured confounders in observational studies. (1998)
Biometrics. 54(3):948-63. PubMed PMID: 9750244.
According to the article, assuming certain parameters (p0, p1, gamma),
one can calculate the corrected coefficient of the Cox regression
using a fairly simple formula :

beta_corr = _b[x] - log((exp(gamma)*p1 + 1 -p1)/(exp(gamma)*p0 +1-p0))

The authors recommend calculating the confidence interval of the
b_corr with the delta method.
If I run the -nlcom- command written like this:

nlcom (beta_corr:

I do indeed get a nice-looking beta_corr and a 95%CI. However, I admit
to doing this without a thorough understanding of the -nlcom- method.
Can I assume that this is indeed the proper 95%CI by delta method? In
particular, should I be worried about the fact that the data is
pweighted? The sample is large enough to accomodate the "large number"

I would appreciate any input or suggestion!
Best regards,
Adam Olszewski
*   For searches and help try:

© Copyright 1996–2016 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index