darft using expertsurv package in vignette

vignettes/basic.Rmd

@@ -119,7 +119,7 @@ plot(ble_Surv2) +
 
 ## flexsurv frequentist background fit
 
-The last example is for an HMC and frequentist survival model using the flexsurv package directly.
+The next example is for an HMC and frequentist survival model using the flexsurv package directly.
 
 ```{r}
 obs_Surv3 <- fit.models(formula = Surv(death_t, death) ~ 1,
@@ -136,3 +136,53 @@ ble_Surv3 <- blendsurv(obs_Surv3, ext_Surv3, blend_interv, beta_params)
 plot(ble_Surv3)
 ```
 
+<!-- TODO -->
+## `{expertsurv}` background fit
+
+The next example is using the [`{expertsurv}`](https://github.com/Philip-Cooney/expertsurv) package to fit the external model.
+
+We can create the external survival curve using the penalised regression model.
+
+```{r eval=FALSE}}
+# devtools::install_github("Philip-Cooney/expertsurv")
+library(expertsurv)
+
+param_expert[[1]] <- data.frame(dist = "norm",
+                                wi = 1,
+                                param1 = 0.1,
+                                param2 = 0.005)
+
+param_expert[[2]] <- data.frame(dist = "norm",
+                                wi = 1,
+                                param1 = 0.05,
+                                param2 = 0.005,
+                                param3 = NA)
+
+timepoint_expert <- c(14,18)
+
+obs_Surv3 <- fit.models(formula = Surv(death_t, death) ~ 1,
+                        data = dat_FCR,
+                        distr = "exponential",
+                        method = "hmc")
+
+# dummy data
+data <- data.frame(time = 1, event = 0)
+
+# don't provide any data so its all based on the prior
+example1  <- fit.models.expert(formula = Surv(time,event) ~ 1,
+                               data = data,
+                               distr = "gomp",
+                               method = "hmc",
+                               iter = 5000,
+                               opinion_type = "survival",
+                               times_expert = timepoint_expert, 
+                               param_expert = param_expert)
+
+ble_Surv3 <- blendsurv(obs_Surv3, ext_Surv3, blend_interv, beta_params)
+
+plot(ble_Surv3)
+```
+
+Alternatively, we could use the `{expertsurv}` functions to fit both the data and the external information together as intended in the `{expertsurv}` package.
+However, to modify this so that it is similar to `{blendr}` we can choose variance of constraint as functions of the blending interval.
+