Parametric modelling with accelerated failed-time models I am a clinician who is trying to fit a model that predicts the absolute risk of individuals developing cardiovascular events at the end of follow up period. 
I came across accelerated failed time models that should enable me to achieve the above purpose. But I don't know how to interpret the output of the model in R. Can anyone help?
How do I generate output that tells me about the absolute predicted risk of individuals?? 
      surfit <- survreg(Surv(time, delta) ~ as.factor(stage) + age, dist='Weibull')
      summary(surfit)                
                      Value Std.Error    z     p
        (Intercept) 3.5288  0.9041     3.903 9.50e-05 
  as.factor(stage)2 -0.1477 0.4076    -0.362 7.17e-01
  as.factor(stage)3 -0.5866 0.3199    -1.833 6.68e-02
  as.factor(stage)4 -1.5441 0.3633    -4.251 2.13e-05
                age -0.0175 0.0128    -1.367 1.72e-01
         Log(scale) -0.1223 0.1225    -0.999 3.18e-01

 A: Because survival::survreg objects have a predict method you can use this:
 indiv.pred <- predict( surfit, type="response")

The 'type' argument is not actually needed, because that is what survreg has as its default value. Similarly you do not need to add a new data argument because the original dataset values will be used as default arguments to 'newdata'.
A: I guess the question arises because of some confusion with the possibilities to parameterise a Weibull survival model. Weibull survival model can be parameterised both as a proportional hazard (PH) model and accelerated failure time (AFT) model. The survreg function in the survival package in R uses the AFT, which means that it is not the hazard rate that is being modelled but time to failure. As such, you do not get an estimate in terms of hazard. 
However, in the case of Weibull survival model, both the PH and AFT result in the same regression model. This means, having the AFT model parameter coefficients one is able to get the coefficients of PH model. This is explained in P. Hougaard, Fundamentals of Survival Data, Biometrics, March 1999, p.18. If the AFT model is: 
$logT_i = \eta' z_i + \epsilon_i$
the PH coefficient $\beta$ (as in $h(t)=exp(\beta'z)$) can be obtained using:
$\eta = - \beta/\gamma$
where $\gamma$ is the shape parameter which is the inverse of scale. 
Perhaps an easier alternative is to use weibreg in eha package which is a PH model from the beginning. 
The delta in your model formula is supposed to be the vector that indicates failure or censoring. 
Hope this helps.
