R Under development (unstable) (2025-02-03 r87683 ucrt) -- "Unsuffered Consequences"
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> library(Umpire)
> # set seed to ensure reproducibility
> suppressWarnings( RNGversion("3.5.3") )
> set.seed(539586)
> # create a cancer model
> x <- CancerModel('test', 30, 10,
+                  HIT  = function(n) 3+rbinom(1, 4, 0.7),
+                  SURV = function(n) rnorm(n, 0, 2),
+                  OUT  = function(n) rnorm(n, 0, 2))
> # describe the model
> print(paste('Number of patterns:', nPatterns(x), '(should equal 10)'))
[1] "Number of patterns: 10 (should equal 10)"
> print(paste('Number of hits:', nPossibleHits(x), '(should equal 30)'))
[1] "Number of hits: 30 (should equal 30)"
> print(nHitsPerPattern(x))
 [1] 6 5 7 6 4 3 7 6 6 7
> summary(x)
test, a CancerModel object constructed via:
   CancerModel(name = "test", nPossible = 30, nPattern = 10, HIT = 
    function(n) 3 + rbinom(1, 4, 0.7), SURV = function(n) rnorm(n, 0, 2), 
    OUT = function(n) rnorm(n, 0, 2)) 

Pattern prevalences:
 [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Survival effects:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-3.8881 -1.0784 -0.2658 -0.3926  0.9417  2.9427 

Outcome effects:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-3.42001 -1.58186 -0.22381  0.04591  1.31585  4.61663 
> # check correlation
> cor(survivalCoefficients(x), outcomeCoefficients(x))
[1] 0.6163946
> plot(survivalCoefficients(x), outcomeCoefficients(x))
> # generate outcome data from the model
> y <- rand(x, 300)
> print(table(y$Outcome, y$CancerSubType))
      
        1  2  3  4  5  6  7  8  9 10
  Bad  28 10 10 22 33 33  0  1 15  2
  Good  0 28 20  0  1  0 29 35 12 21
> if (require(survival)) {
+   plot(survfit(Surv(LFU, Event) ~ Outcome, y))
+   z <- coxph(Surv(LFU, Event) ~ Outcome, y)
+   summary(z)
+ }
Loading required package: survival
Call:
coxph(formula = Surv(LFU, Event) ~ Outcome, data = y)

  n= 300, number of events= 87 

               coef exp(coef) se(coef)      z Pr(>|z|)    
OutcomeGood -2.2031    0.1105   0.3233 -6.814 9.52e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

            exp(coef) exp(-coef) lower .95 upper .95
OutcomeGood    0.1105      9.053   0.05861    0.2082

Concordance= 0.735  (se = 0.018 )
Likelihood ratio test= 73.03  on 1 df,   p=<2e-16
Wald test            = 46.42  on 1 df,   p=1e-11
Score (logrank) test = 67.98  on 1 df,   p=<2e-16

> # cleanup
> rm(x, y, z)
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
    1.4     0.2     1.6