R Under development (unstable) (2025-02-03 r87683 ucrt) -- "Unsuffered Consequences"
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> library(Umpire)
> # set seed to ensure reproducibility
> set.seed(63582)
> 
> sm <- SurvivalModel(baseHazard = 1/5, # default 1/5 inverse years
+                     accrual = 5,      # default 5 years
+                     followUp = 1,     # default 1 years
+                     units = 12, unitName = "months")
> 
> sdata <- rand(sm, 100)
> class(sdata) == "data.frame"
[1] TRUE
> all( dim(sdata) == c(100, 2) )
[1] TRUE
> summary(sdata) # median raw survival == 29 months, 44 events out of 100 patients
      LFU          Event        
 Min.   : 0.00   Mode :logical  
 1st Qu.:16.75   FALSE:56       
 Median :29.00   TRUE :44       
 Mean   :31.39                  
 3rd Qu.:44.25                  
 Max.   :71.00                  
> if(require(survival)) {
+   model <- survfit(Surv(LFU, Event) ~ 1, sdata)
+   print(model)
+   plot(model)
+ }
Loading required package: survival
Call: survfit(formula = Surv(LFU, Event) ~ 1, data = sdata)

       n events median 0.95LCL 0.95UCL
[1,] 100     44     45      41      NA
> 
> sm <- SurvivalModel(1/2, 5, 2, 12, "months")
> sdata <- rand(sm, 100)
> class(sdata) == "data.frame"
[1] TRUE
> all( dim(sdata) == c(100, 2) )
[1] TRUE
> summary(sdata) # median raw survival == 29 months, 44 events out of 100 patients
      LFU          Event        
 Min.   : 0.00   Mode :logical  
 1st Qu.: 8.00   FALSE:11       
 Median :17.00   TRUE :89       
 Mean   :18.73                  
 3rd Qu.:24.50                  
 Max.   :63.00                  
> if(require(survival)) {
+   model <- survfit(Surv(LFU, Event) ~ 1, sdata)
+   print(model)
+   plot(model)
+ }
Call: survfit(formula = Surv(LFU, Event) ~ 1, data = sdata)

       n events median 0.95LCL 0.95UCL
[1,] 100     89     17      15      20
> 
> ## Now test how SurvivalModel works in the CancerModel
> ## return to defaults
> sm <- SurvivalModel(baseHazard = 1/5, # default 1/5 inverse years
+                     accrual = 5,      # default 5 years
+                     followUp = 1,     # default 1 years
+                     units = 12, unitName = "months")
> R <- rand(sm, 200)
> summary(R)
      LFU          Event        
 Min.   : 1.00   Mode :logical  
 1st Qu.:15.00   FALSE:106      
 Median :28.00   TRUE :94       
 Mean   :30.43                  
 3rd Qu.:45.00                  
 Max.   :71.00                  
> 
> cm <- CancerModel("survtester", nPossible=20, nPattern=2,
+                   SURV = function(n) rnorm(n, 0, 0.3),
+                   survivalModel = sm)
> S <- rand(cm, 200)
> summary(S)
 CancerSubType  Outcome         LFU          Event        
 Min.   :1.00   Bad :103   Min.   : 0.00   Mode :logical  
 1st Qu.:1.00   Good: 97   1st Qu.: 5.00   FALSE:37       
 Median :2.00              Median :12.00   TRUE :163      
 Mean   :1.53              Mean   :15.94                  
 3rd Qu.:2.00              3rd Qu.:23.25                  
 Max.   :2.00              Max.   :65.00                  
> attach(S)
> table(CancerSubType, Outcome)
             Outcome
CancerSubType Bad Good
            1  48   46
            2  55   51
> detach()
> if(require(survival)) {
+   model <- survfit(Surv(LFU, Event) ~ CancerSubType, S)
+   print(model)
+   plot(model, col=2:3)
+ }
Call: survfit(formula = Surv(LFU, Event) ~ CancerSubType, data = S)

                  n events median 0.95LCL 0.95UCL
CancerSubType=1  94     71     18       9      28
CancerSubType=2 106     92     11       8      14
> 
> ## Now test how SurvivalModel works in the ClinicalEngine
> 
> ce <- ClinicalEngine(20, 4, TRUE, survivalModel = sm)
> summary(ce)
A 'CancerEngine' using the cancer model:
--------------
Clinical Simulation Model (Raw), a CancerModel object constructed via:
   CancerModel(name = "Clinical Simulation Model (Raw)", nPossible = NP, 
    nPattern = nClusters, HIT = hitfn, SURV = SURV, OUT = OUT, 
    survivalModel = survivalModel, prevalence = Prevalence(isWeighted, 
    nClusters)) 

Pattern prevalences:
[1] 0.2585102 0.2920282 0.1668301 0.2826315

Survival effects:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.85050 -0.18776 -0.07136 -0.01801  0.25903  0.65327 

Outcome effects:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.40705 -0.26531 -0.09070 -0.09424  0.07027  0.22813 
--------------

Base expression given by:
An Engine with 10 components.

Altered expression given by:
An Engine with 10 components.
> X <-  rand(ce, 200)
> summary(X$clinical)
 CancerSubType   Outcome         LFU          Event        
 Min.   :1.000   Bad : 80   Min.   : 0.00   Mode :logical  
 1st Qu.:1.000   Good:120   1st Qu.:15.00   FALSE:115      
 Median :2.000              Median :28.00   TRUE :85       
 Mean   :2.415              Mean   :29.93                  
 3rd Qu.:3.000              3rd Qu.:41.00                  
 Max.   :4.000              Max.   :71.00                  
> 
> proc.time()
   user  system elapsed 
   1.39    0.29    1.62