[R-sig-Epi] Question about AFT models

[Lisa van der Burgh]

Hello everyone,


I am doing a survival analysis at the moment. I want to make an AFT model in R. I have fitted the following model:

fit1 <- survreg(Surv(TimeVentilator, event) ~ (age + sex + mutation + BM1 + BM2)^2, data = DF,
                dist = "weibull")
summary(fit1)


I want to test if the weibull distribution is the good distribution. I tested this in the following way:

fits <- fit1$linear.predictors
resids <- (fit1$y[, 1] - fits) / fit1$scale
resKM <- survfit(Surv(resids, event = ) ~ 1, data = DF)

plot(resKM, mark.time = FALSE, xlab = "AFT Residuals",
     ylab = "Survival Probability", main = "Dataset: Pompe Disease")

xx <- seq(min(resids), max(resids), length.out = 35)
yy <- exp(- exp(xx))
lines(xx, yy, col = "red", lwd = 2)



I saw that the weibull distribution is not a good distribution. I found that I can also test other distributions by changing the yy command.


I found this:

if you have fitted an AFT models assuming the log-normal distribution, you should use yy <- pnorm(xx, lower.tail = FALSE), and if you have fitted an AFT model assuming the log-logistic distribution, you should use yy <- plogis(xx, lower.tail = FALSE).


So I tried that, but still, the distributions do not fit my data.

Can you help me how to change the yy so that I can test other distributions for the AFT model? Like the students t distribution. I found the possible distributions, but I cannot find the R code for this.


I really hope that someone can help me with this!


Thankyou in advance,


Best,

Lisa


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