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.
flexsurvreg()function? At the moment the output (from
summarypresumably on the fitted object) doesn't have any standard errors printed, which seems odd, and these would need to be calculated before any statistical inference could be done (confidence intervals, hypothesis tests) $\endgroup$