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From |
Adam Olszewski <adam.olszewski@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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: _b[xrt]-log((exp(`gamma')*`p1'+1-`p1')/(exp(`gamma')*`p0'+1-`p0'))), post 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" assumption. I would appreciate any input or suggestion! Best regards, Adam Olszewski * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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