MILC (version 1.0)

nat_hist: Simulate one individual trajectory

Description

Function to run the MILC model and predict a full lung cancer trajectory depending on the age, gender and smoking history of an individual (see MILC-package for more details).

Usage

nat_hist(dat, pred_yrs, gender, status, ts, tq, m, cdiagn, creg, cdist)

Arguments

dat

7-dimensional numeric vector, with the first 5 values being random numbers from Unif[0,1], required for the simulation, and the 6th and 7th value referring to the age (years) and smoking intensity (number of cigarettes) respectively.

pred_yrs

prediction period (years)

gender

"male" or "female"

status

smoking status, i.e., whether the person is "never", "former", or "current" smoker

ts

start smoking age (years), when relevant

tq

quit smoking age (years), when relevant

m

scale parameter of the Gompertz distribution assumed for the tumor growth

cdiagn

2-dimensional vector with location and scale parameters of the log-Normal distribution assumed for the tumor volume at diagnosis

creg

2-dimensional vector with location and scale parameters of the log-Normal distribution assumed for the tumor volume at the beginning of the regional stage

cdist

2-dimensional vector with location and scale parameters of the log-Normal distribution assumed for the tumor volume at the beginning of the distant stage

Value

[[1]]: T-entry: age(years) at the beginning of the prediction period[[2]]: T_mal: age(years) at the onset of the first malignant cell[[3]]: T_reg: age(years) at the beginning of the regional stage[[4]]: T_dist: age(years) at the beginning of the distant stage[[5]]: T_diagn: age(years) at diagnosis[[6]]: D_diagn: tumor diameter at diagnosis[[7]]: stage: tumor stage at diagnosis[[8]]: T-pred: age(years) at the end of the prediction period[[9]]: T_do: predicted age(years) at death from a cause other than lung cancer[[10]]: T_dl: predicted age(years) at death from lung cancer[[11]]: T_final: age (years) at the end of the simulated trajectory[[12]]: lung_inc: whether the person developed (1="Yes") lung cancer or not (0="No")[[13]]: excl: exclude unreasonable cases ("Yes", "No")[[14]]: cause: cause of death ("lung", "other", NA)[[15]]: T_death: age(years) at death from any cause[[16]]: gender[[17]]: smoking status[[18]]: start smoking age(years)[[19]]: quit smoking age(years)[[20]]: smoking intensity (number of cigarettes)

See Also

t_mal, t_prog, tdeath_other, tdeath_lung

Examples

# In the following examples we predict lung cancer trajectories for a man, 50 years old 
# at the beginning of the prediction period, who has started smoking at the age of 20 years
# and smokes 30 cigerettes per day on average. The model predicts 20 years ahead. 

# We present three possible trajectories:

# In the first case the person does not die before the end of the prediction period:
set.seed(33)
nat_hist ( c(runif(5),50,30), 20, "male", "current", 20, NA, 
		0.00042, c(3.91, 3.91), c(1.1, 1.1), c(2.8, 2.8))

# In the second case the person dies at the age of 62.43 years from lung cancer:
set.seed(1470)
nat_hist ( c(runif(5),50,30), 20, "male", "current", 20, NA, 
		0.00042, c(3.91, 3.91), c(1.1, 1.1), c(2.8, 2.8))

# In the third case the person dies at the age of 69.68 years from a cause
# other than lung cancer:
set.seed(1450)
nat_hist ( c(runif(5),50,30), 20, "male", "current", 20, NA, 
		0.00042, c(3.91, 3.91), c(1.1, 1.1), c(2.8, 2.8))