I have a methodological question, and therefore no sample dataset is attached. I'm planning to do a propensity score adjusted Cox regression that aims to examine whether a certain drug will reduce the risk of an outcome. The study is observational, comprising 10,000 individuals. The data set contains 60 variables. I judge that 25 of … Read more
How one can create a logrank test for trend and does it differ from normal logrank test? Any suggestions or literature? Maybe some R examples and functions? Best Answer Try comp from survMisc package. It extends the survival package. It counts statistic and p-value for logrank test, as well as for Gehan-Breslow, Tarone-Ware, Peto-Peto and … Read more
When dealing with a Cox regression model, we have to assume that the proportionality assumption holds. I have read about a lot of methods to check if this assumption holds or not but I have not really understood what does this assumption mean in practice. My professor explained it as the fact that given that … Read more
I am performing survival analysis on credit data. I created a simple model with using interest rate: cox <- coxph(Surv(periods,charged_off) ~ int_rate, data=notes) I assumed that int_rate was a time-independent variable, but the following test rejects HA: > cox.zph(cox) rho chisq p int_rate 0.0446 14.2 0.000169 Same result for other variables such as loan amount: … Read more
I would appreciate if anyone can share a reference that discusses magnitudes of hazard ratios as effect sizes. Specific subject matter obviously weighs in when trying to determine what a relevant effect size is, but the problem I have seen in some recent papers is that time-to-event Cox PH analyses with large sample sizes (i.e., … Read more
I am trying to use two variables – activity score (ascore – a whole number indicating amount of activity) and gini (given by Gini-Simpson index – a value ranging between 0 and 1, indicating diversity of activity) to predict number of days they survived. Call: coxph(formula = Surv(NumDays, failed) ~ ascore + gini, data = … Read more
I am trying to use two variables – activity score (ascore – a whole number indicating amount of activity) and gini (given by Gini-Simpson index – a value ranging between 0 and 1, indicating diversity of activity) to predict number of days they survived. Call: coxph(formula = Surv(NumDays, failed) ~ ascore + gini, data = … Read more
Assuming that I have a data set of people with the following features: age (numeric) education (factor) time an 'event' happened to them(could happen multiple times in the recorded time of the person). The time is measured in months. and I want to predict when this event will luckily happen again in a time domain(ex. … Read more
Assuming that I have a data set of people with the following features: age (numeric) education (factor) time an 'event' happened to them(could happen multiple times in the recorded time of the person). The time is measured in months. and I want to predict when this event will luckily happen again in a time domain(ex. … Read more
I have gone through WinBugs documentation (for example, http://www.mrc-bsu.cam.ac.uk/bugs/thebugsbook/examples/html/Chapter-11-Specialised/Example-11_7_2-leukaemia.html). And also through this book (http://www.amazon.ca/Bayesian-Survival-Analysis-Joseph-Ibrahim/dp/0387952772). Both use a gamma distribution prior for the rate (lambda) for the exponential distribution. They alternate between Gamma(0.01,0.01) and Gamma(0.001,0.001). I would like to use a non-informative prior…. but I don't think this is it? Could someone explain whether this … Read more