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| From | "enzo.coviello@tin.it" <enzo.coviello@tin.it> |
| To | <statalist@hsphsun2.harvard.edu> |
| Subject | R: Re: st: RE: Cox regression for grouped survival data |
| Date | Fri, 9 Aug 2013 08:23:43 +0200 (CEST) |
Dear Steve, thanks for your attention you can be as semi-parametric as you'd like with -cloglog-. With enough data you can fit a separate integrated baseline hazard to each interval. Ok, almost agree. But a parsimonious approach is to fit a flexible polynomial to the period terms, perhaps a fractional polynomial (-fp-). In a similar spirit, the packages -stpm- (Royston) and -stpm2- (Lambert) at SSC fit restricted cubic splines to the survival function. Yes, the FPM are a great tool for survival analysis, but we can use this approach for data at individual level. Here we are using grouped data, i.e data where we know the number of events and censored observations within intervals. I believe that this kind of data are used by Feuer and coll. to develop the "profile of prognostic factors" within the Cancer Survival Query Siystem. Enzo The shortcomings of the the standard Kaplan-Meier and Breslow estimates (Breslow, 1972) were presented by Lambert and Royston at the 2009 UK Stata meetings. Cox himself preferred a parametric approach (Reid, 1994, p. 450), as did Efron (1988). References: Breslow, NE. 1972. Contribution to the discussion of the paper by DR Cox. Journal of the Royal Statistical Society, Series B 34, no. 2: 216-217. Efron, Bradley. 1988. Logistic Regression, Survival Analysis, and the Kaplan-Meier Curve. Journal of the American Statistical Association Journal of the American Statistical Association 83, no. 402: 414-425. Lambert, P. C., & Royston, P. (2009). Flexible parametric alternatives to the Cox model. UK Stata User Group. http://www.stata.com/meeting/uk09/uk09_lambert_royston.pdf Reid, Nancy. 1994. A conversation with Sir David Cox. Statistical Science 9, no. 3: 439-455. Steve > On Aug 8, 2013, at 11:11 AM, enzo.coviello@tin.it wrote: > > Hi Pradip, > > thanks for further reference. > > I believe that the approach suggested by Steve may be applied to this > situation (the work of prof. Jenkins in this field is precious), but in > strict sense it is a parametric approach. > > The approach followed by Feuer and al. should be semiparametric, that > is a Cox-like model. > > Enzo > > ----Messaggio originale---- > > Da: Pradip.Muhuri@samhsa.hhs.gov > > Data: 8-ago-2013 15.23 > > A: "statalist@hsphsun2.harvard.edu"<statalist@hsphsun2.harvard.edu> > > Ogg: st: RE: Cox regression for grouped survival data > > Hello, > > The following article has also used Cox regression for grouped survival > analysis of the NHIS-Linked Mortality Public Use Data Files. > > "21st-Century Hazards of Smoking and Benefits of Cessation in the > United Stateshttp://www.nejm.org/doi/full/10.1056/NEJMsa1211128#t=articleMethods > > Recently, Steve Samuels (StataList) has suggested to me that I use > complementary log log models (-cloglog-) for my analysis of the same > data (mentioned above) but for a different topic. > > The following is from Steve, " see the Lesson 6 link to discrete data > analysis on Stephen Jenkins's fine web page "Survival analysis with > Stata"" > > (http://www.iser.essex.ac.uk/survival-analysis. > -- > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner- > statalist@hsphsun2.harvard.edu] On Behalf Of enzo.coviello@tin.it > > Sent: Thursday, August 08, 2013 3:26 AM > > To: statalist@hsphsun2.harvard.edu > > Subject: st: Cox regression for grouped survival data > > Dear Stata Users, > > recently,for developping the Cancer Surveillance Query System, Feuer > and coll. of the NCI used a Cox Regression for Grouped survival data > (Cancer. 2012 Nov 15;118(22):5652-62. doi: 10.1002/cncr.27615. Epub > 2012 May 8). > > Cox model can be sensitive to tied survival times and often we know > only the number of events occurring within time intervals. > > Often we can read that Cox model is really suitable for data where time > is continuous. > > I would ask further addresses on this topic and mainly if exists a > > Stata command-resource to fit a Cox regression for grouped survival > > data. > > Many thanks. > > Enzo > > ^^^^^^^^^^^^^^^^ * * 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/ * * 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/