Counterintuitive results of parametric survival models and Cox regression models

[Gilles-Protais]

Greetings,

I am running parametric survival models and Cox regression models, and I get counterintuitive results; what might be the reason, please? And in my data set I have just 8 events out of 630 individuals

[ballardw]

Good would be to show the code used. That way someone familiar to the procedure could ask pointed questions.

Better would be to show the code and all the notes from the LOG, pasted into a text box opened on the forum with the </> icon above the message window. That would show the answer to some of the questions you should answer like "What were the warnings in the log?"

Best would be to include the log and the output so we can see the summary information and the values that you think are counterintuitive. Discuss which is counterintuitive and why you think so.

[Gilles-Protais]

I tried running the cox regression using the frailty with penalized likelihood and this code

frailtyPenal(Surv(days_events,OUTCOME)~cluster(id)+resqualintra,n.knots=1, kappa=100,data=base)

Moreover, I tried running a log-normal survival model and this is code

(brack.AFT.GF <- flexsurvreg(Surv(days_events, OUTCOME) ~ resqualintra, data=base, dist="lnorm"))

log-normal survivalmodelCox Frailty with penalized likelihood model

As we observe, the parametric model I have a negative estimate and for the Frailty model I have a Positive estimate;

SO this is my issue,What could be an explanation to this?

[Season]

I saw your screenshots and codes and found it was done on R. This is a forum of SAS users. SAS and R are distinctly different statistical softwares. It is true that many SAS users are also familiar with R, but there are also many (e.g., me) who hardly know anything about R. It would be better if you post your questions in forums of R users to get a more rapid answer.