Perform luck adjustment

Usage

luck_adj(prevsurv, cursurv, luck, condq = TRUE)

Arguments

prevsurv: Value of the previous survival
cursurv: Value of the current survival
luck: Luck used to be adjusted (number between 0 and 1)
condq: Conditional quantile approach or standard approach

Value

Adjusted luck number between 0 and 1

Details

This function performs the luck adjustment automatically for the user, returning the adjusted luck number. Luck is interpreted in the same fashion as is standard in R (higher luck, higher time to event).

Note that if TTE is predicted using a conditional quantile function (e.g., conditional gompertz, conditional quantile weibull...) prevsurv and cursurv are the unconditional survival using the "previous" parametrization but at the previous time for presurv and at the current time for cursurv. For other distributions, presurv is the survival up to current time using the previous parametrization, and cursurv is the survival up to current time using the current parametrization.

Note that the advantage of the conditional quantile function is that it does not need the new parametrization to update the luck, which makes this approach computationally more efficient. This function can also work with vectors, which could allow to update multiple lucks in a single approach, and it can preserve names

Examples

luck_adj(prevsurv = 0.8,
 cursurv = 0.7,
 luck = 0.5,
 condq = TRUE)
#> [1] 0.4285714
 
luck_adj(prevsurv = c(1,0.8,0.7),
 cursurv = c(0.7,0.6,0.5),
 luck = setNames(c(0.5,0.6,0.7),c("A","B","C")),
 condq = TRUE)
#>         A         B         C 
#> 0.2857143 0.4666667 0.5800000 
 
luck_adj(prevsurv = 0.8,
 cursurv = 0.7,
 luck = 0.5,
 condq = FALSE) #different results
#> [1] 0.5625

#Unconditional approach, timepoint of change is 25,
# parameter goes from 0.02 at time 10 to 0.025 to 0.015 at time 25,
#  starting luck is 0.37
new_luck <- luck_adj(prevsurv = 1 - pweibull(q=10,3,1/0.02),
 cursurv = 1 - pweibull(q=10,3,1/0.025),
 luck = 0.37,
 condq = FALSE) #time 10 change
 
new_luck <- luck_adj(prevsurv = 1 - pweibull(q=25,3,1/0.025),
 cursurv = 1 - pweibull(q=25,3,1/0.015),
 luck = new_luck,
 condq = FALSE) #time 25 change
 
qweibull(new_luck, 3, 1/0.015) #final TTE 
#> [1] 43.52338

#Conditional quantile approach 
new_luck <- luck_adj(prevsurv = 1-pweibull(q=0,3,1/0.02),
                      cursurv = 1- pweibull(q=10,3,1/0.02),
                      luck = 0.37,
                      condq = TRUE) #time 10 change, previous time is 0 so prevsurv will be 1

new_luck <- luck_adj(prevsurv = 1-pweibull(q=10,3,1/0.025),
                      cursurv = 1- pweibull(q=25,3,1/0.025),
                      luck = new_luck,
                      condq = TRUE) #time 25 change

qcond_weibull(rnd = new_luck,
                     shape = 3,
                     scale = 1/0.015,
                     lower_bound = 25) + 25 #final TTE
#> [1] 43.52338