Problem plotting curve on survival curve (something silly?)
Calum wrote:
All of that is very nice so far. The I followed bits and pieces of other peoples posts in the past to plot on a weibull regression...
> my_curve.Plac <- survreg( Surv(Survival, Censored==0)~
TreatmentGroup, subset=TreatmentGroup=="Placebo", data=TestData, dist='weibull')
Peter wrote:
I'd take a hard look at the pweibull(...) bit. Is "scale" really what you want it to be? If coef(my_curve.Pred) is not a scalar, then it gets recycled, which could easily cause oscillations.
Aha - found it. Its from my attempt to subgroup the data before I discovered subset... I now have a curve instead of an oscillation. Now possibly that curve is wrong... will need to do more reading! For reference the line above should read: > my_curve.Plac <- survreg( Surv(Survival, Censored==0)~ 1, subset=TreatmentGroup=="Placebo", data=TestData, dist='weibull')
Also is it possible to get an R-squared type value for the fit of this curve from someplace? Finally (three questions in one!) the first two censored data points (1 in each group) are actually lost to follow-ups. Should they be marked differently from censored?
Customarily they are not. (I'm sure it is possible to speculate at length about it, though.)
Going off topic a bit - but did you mean customarily they are not censored or customarily they are not handled differently from censored!
Nothing spectacularly incompetent this far... (I'm not happy with R^2 measures outside of linear models, or even within linear models, but several well-reputed people do find them useful, so who am I to bicker?)
I'm not competent to argue. But are you suggesting there is a better way to assess fit of the line to the data? Thats what I want - Not being a statistician I'm not fussed how its done. But If I'm going to extrapolate a line I'd like to know its a reasonable fit first (is that purely by eye?) There is a p value reported by survreg but no idea how to interpret it ;-)