On Sun, Aug 15, 2004 at 02:28:03PM +0200, Christoph Scherber wrote: > Dear all, > > How can I analyze a life table (e.g. for a cohort of insects) in R? > > I have 20 insects in 200 cages with two different treatments, whose > survival is followed over time, such that, e.g., in one treatment, the > number of animals surviving is c(20,18,16,12,10,8,4,0), while in the > other treatment the survival is c(20,20,18,18,16,15,15,14) at 8 > subsequent time intervals.
One way of doing it would be to create an individual-based data frame from your two life tables, which would give you > dat enter exit event treatment 1 0 1 1 1 2 0 1 1 1 3 0 2 1 1 4 0 2 1 1 5 0 3 1 1 6 0 3 1 1 7 0 3 1 1 8 0 3 1 1 9 0 4 1 1 10 0 4 1 1 11 0 5 1 1 12 0 5 1 1 13 0 6 1 1 14 0 6 1 1 15 0 6 1 1 16 0 6 1 1 17 0 7 1 1 18 0 7 1 1 19 0 7 1 1 20 0 7 1 1 21 0 2 1 2 22 0 2 1 2 23 0 4 1 2 24 0 4 1 2 25 0 5 1 2 26 0 7 1 2 27 0 7 0 2 28 0 7 0 2 29 0 7 0 2 30 0 7 0 2 31 0 7 0 2 32 0 7 0 2 33 0 7 0 2 34 0 7 0 2 35 0 7 0 2 36 0 7 0 2 37 0 7 0 2 38 0 7 0 2 39 0 7 0 2 40 0 7 0 2 where 'exit' represents the ordered time intervals. Then you can choose between a discrete-time (recommended) and a continuous-time Cox regression. In package 'eha' that corresponds to the functions 'mlreg' and 'coxreg', respectively. Output from mlreg: ---------------------------------------------------------------------- > mlreg(Surv(enter, exit, event) ~ treatment, data = dat) Call: mlreg(formula = Surv(enter, exit, event) ~ treatment, data = dat) Covariate Mean Coef Rel.Risk Wald p treatment 1 0.419 0 1 (reference) 2 0.581 -2.121 0.120 0.000 Events 26 Total time at risk 210 Max. log. likelihood -64.734 LR test statistic 21.9 Degrees of freedom 1 Overall p-value 2.82840e-06 ----------------------------------------------------------------------- This corresponds essentially to a binomial regression approach, where you regard the number of deaths in each time interval for each treatment as the outcome of a binomial experiment with n = riskset size at the beginning of the interval. An alternative would be Poisson regression with riskset size as an offset. -- Göran Broström tel: +46 90 786 5223 Department of Statistics fax: +46 90 786 6614 Umeå University http://www.stat.umu.se/egna/gb/ SE-90187 Umeå, Sweden e-mail: [EMAIL PROTECTED] ______________________________________________ [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