[R] survdiff for Left-truncated and right-censored data
dear all, I would like to know whether survdiff and survReg function in the survival package work for left-truncated and right-censored data. If not, what other functions can i use to make comparison between two survival curves with LTRC data. thanks for any help given sing yee __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] survdiff for Left-truncated and right-censored data
On Wed, 23 Nov 2005, Sing-Yee Ling wrote: dear all, I would like to know whether survdiff and survReg function in the survival package work for left-truncated and right-censored data. The survdiff and survfit functions do, as does coxph. There is no survReg in the survival package, but there is survreg, and it does not handle left truncation. -thomas If not, what other functions can i use to make comparison between two survival curves with LTRC data. thanks for any help given sing yee __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] survdiff
On Tue, 17 Aug 2004, Peter Dalgaard wrote: You really need to read a theory book for this, but here's the basic idea: V is the theoretical variance of O-E for the first group. If O-E is approximately normally distributed, as it will be in large samples, then (O-E)^2/V will be approximately chi-squared distributed on 1 DF. In *other* models, notably those for contingency tables, the same idea works out as the familiar sum((O-E)^2/E) formula. That formula has historically been used for the logrank test too, and it still appears in some textbooks, but as it turns out, it is not actually correct (although often quite close). You don't necessarily need a theory book --- sufficiently old biostat textbooks may have this. For example, Fisher van Belle (1993) Biostatistics: a methodology for the health sciences gives both formulas and explains that the simpler one is a useful approximation for hand calculation, with a worked example. Now that we have computers no-one needs to use the approximation, and most of that information has been taken out of the second edition. -thomas __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] survdiff
Krista Haanstra [EMAIL PROTECTED] writes: As I am quitte an ignorant user of R, excuse me for any wrongfull usage of all the terms. My question relates to the statistics behind the survdiff function in the package survival. My textbook knowledge of the logrank test tells me that if I want to compare two survival curves, I have to take the sum of the factors: (O-E)^2/E of both groups, which will give me the Chisq. If I calculate this by hand, I get a different value than the one R is giving me. Actually, the (O-E)^2/E that R gives me, those I agree with, but if I then take the sum, this is not the chisq R gives. Two questions: - How is Chisq calculated in R? - What does the column (O-E)^2/V mean? What is V, and how does this possibly relate to the calculated Chisq? You really need to read a theory book for this, but here's the basic idea: V is the theoretical variance of O-E for the first group. If O-E is approximately normally distributed, as it will be in large samples, then (O-E)^2/V will be approximately chi-squared distributed on 1 DF. In *other* models, notably those for contingency tables, the same idea works out as the familiar sum((O-E)^2/E) formula. That formula has historically been used for the logrank test too, and it still appears in some textbooks, but as it turns out, it is not actually correct (although often quite close). [To fix ideas, consider testing for a given p in the binomial distribution, you can either say O=x E=np V=npq and get chisq = (x-np)^2/npq or have O = (x, n-x), E = (np, nq) and get chisq = (x-np)^2/np + ((n-x) - nq)^2/nq and a little calculus show that the latter expression is = (x-np)^2*(1/np + 1/nq) = (x-np)^2 * (p+q)/npq so the two formulas are one and the same. In this case!] -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
