I am using R's flexsurvreg function (in the flexsurv package) to fit a AFT model to my data.
This is the line of code that fits the model to the data:
TestModel <- flexsurvreg(Surv(time,death) ~ param1 + param2 + param3 + param4 + param5 + param6 + param7 + param8 + param9 + param10 + param11 + param12 + param13, data = DataTest, dist = "weibull")
Once the model fits, this is a summary of the results:
Estimates: data mean est L95% U95% se exp(est) L95% U95% shape NA 9.99e-01 NA NA NA NA NA NA scale NA 2.20e+02 NA NA NA NA NA NA param1 1.32e-01 2.51e-01 NA NA NA 1.29e+00 NA NA param2 1.61e-01 -1.54e-02 NA NA NA 9.85e-01 NA NA param3 1.89e-01 -4.68e-02 NA NA NA 9.54e-01 NA NA param4 1.76e-01 -2.25e-02 NA NA NA 9.78e-01 NA NA param5 1.87e-01 -5.35e-02 NA NA NA 9.48e-01 NA NA param6 7.56e-01 -2.74e-01 NA NA NA 7.60e-01 NA NA param7 2.28e-01 3.23e-02 NA NA NA 1.03e+00 NA NA param8 1.58e-01 -1.69e-02 NA NA NA 9.83e-01 NA NA param9 4.32e-01 -1.89e-02 NA NA NA 9.81e-01 NA NA param10 1.30e+02 -1.01e-03 NA NA NA 9.99e-01 NA NA param11 2.26e+01 -4.08e-03 NA NA NA 9.96e-01 NA NA param12 5.54e+02 -2.84e-04 NA NA NA 1.00e+00 NA NA param13 9.57e+01 -4.69e-03 NA NA NA 9.95e-01 NA NA N = 40320, Events: 32154, Censored: 8166 Total time at risk: 2584693 Log-likelihood = -171611.5, df = 15 AIC = 343253.1
I want to measure how the covariates affect the survival time. The estimates provide an understanding of this. Also, as I read here, $exp(est)$ provides an estimate of how the hazard changes with change in 1 unit of a covariate by keeping the other covariates fixed. Is there a way I can calculate p-values for these covariates?
I have fitted a Weibull distribution to my dataset.