Thank you for your time, Thomas . In case the questioner is not aware of a few facts... Thomas Lumley is both a) the person who originally ported tThereau's "survival" package to R and was also its maintainer for many years , and b) the author of the "survey" package
-- David Sent from my iPhone On Mar 18, 2012, at 5:51 PM, Thomas Lumley <tlum...@uw.edu> wrote: > On Mon, Mar 19, 2012 at 10:27 AM, David Winsemius > <dwinsem...@comcast.net> wrote: >> >> On Mar 18, 2012, at 3:54 PM, Thomas Lumley wrote: >> >>> On Mon, Mar 19, 2012 at 6:34 AM, David Winsemius <dwinsem...@comcast.net> >>> wrote: >>>> >>>> >>>> On Mar 16, 2012, at 1:09 PM, niloo javan wrote: >>>> >>>>> hi >>>>> i want to analyze Right Censore-Length bias data under cox model with >>>>> covariate. >>>>> what is the package ? >>>> >>>> >>>> >>>> I initially left this question alone because I thought there might be >>>> viewers for whom it all made perfect sense. After two days that >>>> probability >>>> seems to be declining. The problem I had was the meaning of "length bias >>>> data". Are you talking about a non-proportional effect in which the >>>> assumption of a constant hazard ratio over time is false and other >>>> methods >>>> are needed. If that is correct, then you should get a copy of Therneau >>>> and >>>> Grambsch's "Modeling Survival Data" and study the chapter on "Functional >>>> Form'. The package would be "survival". >>>> >>> >>> Length-biased sampling is what you get when you take a cross-sectional >>> sample of an ongoing process -- long intervals are over-represented. >> >> >> Thank you Thomas; >> >> For example people who have survived to age 75 might be systematically >> different with respect to both the distribution of cardiovascular risk >> factors and their impact on the event of interest (AMI. CV death, or >> all-cause mortality) than persons at age 45. And that would also not take >> into account the fact those risk factors might have changed over the >> interval from age 45 to age 75 in the survivors? >>> >>> >>> If the arrival time is known for everyone in the sample, the usual Cox >>> model facilities for left truncation apply. If the arrival times are >>> not known it would be much more difficult, and would probably need >>> parametric modelling. >> >> >> Am I correct in thinking that additional assumptions about the >> "length-bias" would need to be explicitly stated or modeled under a set of >> plausible scenarios before progress in any framework could be anticipated? >> It would seem that there could be many forms of such a "length-bias". >> > > Yes, as with any missing data problem things can go arbitrarily badly wrong. > > The classical 'length-biased sampling' problem is a cross-sectional > sample from a stationary population process, and that gives good > results. > > Obviously if you don't recruit anyone before time T, there is no > information about what happened before then, but there may still be > useful information afterwards. A good example is the research project > on after-effects of the nuclear bombings of Nagasaki and Hiroshima, > where recruitment started (IIRC) 5 years after the event. There's no > information on survival in the first five years, but very good > subsequent information. > > -thomas > > -- > Thomas Lumley > Professor of Biostatistics > University of Auckland ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
