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His chapter on parametric survival models describes a variety of plotting techniques for checking proportional hazards assumptions and for examining the linearity of estimated log-hazard effects on the …

Referring to the one-sample and two-sample sections of chapter 7 in Klein and Moeschberger's "Survival Analysis", we read that the Peto-Peto version and the Gehan versions were both two-sample (censored … ) versions of the Mann-Whitney Wilcoxon two-sample test but used different versions of the survival function estimator. …

Prior CV postings on the matter of GOF measures in generalized linear models: Find out pseudo R square value for a Logistic Regression analysis Which pseudo-$R^2$ measure is the one to report for lo …

Harrell would advise that you NOT do so: How to do ROC-analysis in R with a Cox model Doing model comparison with LR statistics is more powerful than using methods that depend on an asymptotic distr …

Most R regression functions have an associated predict method and survival::survreg is no exception. … It also shows how to plot expected survival curves and if you picked out the expected 50% survival you would have the predicted median values. …

The premise of the question is wrong. (So read Wikipedia with a critical eye. Most of the rest of that article appears correct, but that sentence is flawed as is the one immediately preceding it that …

@chl has pointed to a specific answer to your question. The 'rms' package's cph function will produce a Somers-D which can be transformed trivially into a c-index. However, Harrell (who introduced the …

When interpreting the output of cox.zph it is just as much (or even more) the "flatness" of the line, as it is the straightness of the line, that is important. If the line is straight but slanted upwa …

When considering problems with survival analyses where estimates blow up, it's often useful to look at tabular displays. (in this case the "explosion" is to the small side rather than the high side.) …

The methods description does not match up with anything I see in Crawley's chapter on survival analysis. … I do not see that the multiple comparisons problem is specific to survival analysis. …

To get the risk estimate for a member of the stratum you would take the decrement in survival divided by the time over which the decrement occurred and divide that by the starting survival fraction. $$ … \frac{ -\frac{d(S(t))}{dt} }{ S(t) } $$ Generally one would smooth the survival fraction as a function of time ( S(t) ), and take the slope of the smoothed estimate as the numerator and the value of the …

(That is an egregious violation of the non-informative censoring assumption needed for survival analysis.) …

Here is a stratified model build based on the tiny test set you offered library(survival) gender <- as.factor(c("Female", "Male", "Female", "Male", "Male", "Male", "Female", "Female", "Female", "Male") … Therneau & Gambsch's text "Modeling Survival Data" has an excellent chapter. …

The default coding of factor variables in R is "treatment contrasts" relative to the first level in the factor definition. There are many other possible contrast arrangements possible. This is not spe …

Shortening the time interval in this case will not reduce the power of test because statistical power in survival analysis comes from the numbers of events rather than the duration of the study. …