Simulate health state probabilities from a survival object using partitioned survival analysis.

# S3 method for survival
sim_stateprobs(x, ...)

## Arguments

x An object of class survival. Further arguments passed to or from other methods.

## Value

A stateprobs object.

## Details

In an $$N$$-state partitioned survival model there are $$N-1$$ survival curves and $$S_n(t)$$ is the cumulative survival function denoting the probability of survival to health state $$n$$ or a lower indexed state beyond time $$t$$. The probability that a patient is in health state 1 at time $$t$$ is simply $$S_1(t)$$. State membership in health states $$2,\ldots, N -1$$ is calculated as $$S_n(t) - S_{n-1}(t)$$. Finally, the probability of being in the final health state $$N$$ (i.e., the death state) is $$1-S_{N-1}(t)$$, or one minus the overall survival curve.

In some cases, the survival curves may cross. hesim will issue a warning but the function will still run. Probabilities will be set to 0 in a health state if the prior survival curve lies above the curve for state $$n$$; that is, if $$S_n(t) < S_{n-1}(t)$$, then the probability of being in state $$n$$ is set to 0 and $$S_n(t)$$ is adjusted to equal $$S_{n-1}(t)$$. The probability of being in the final health state is also adjusted if necessary to ensure that probabilities sum to 1.

survival

## Examples

library("data.table")
library("survival")

# This example shows how to simulate a partitioned survival model by
# manually constructing a "survival" object. We will consider a case in which
# Cox proportional hazards models from the survival package---which are not
# integrated with hesim---are used for parameter estimation. We will use
# point estimates in the example, but bootstrapping, Bayesian modeling,
# or other techniques could be used to draw samples for a probabilistic
# sensitivity analysis.

# (0) We first setup our model per usual by defining the treatment strategies,
# target population, and health states
hesim_dat <- hesim_data(
strategies = data.table(strategy_id = 1:3,
strategy_name = c("SOC", "New 1", "New 2")),
patients = data.table(patient_id = 1:2,
female = c(0, 1),
grp_id = 1),
states = data.table(state_id = 1:2,
state_name = c("Stable", "Progression"))
)

# (1) Next we will estimate Cox models with survival::coxph(). We illustrate
# by predicting progression free survival (PFS) and overall survival (OS)
## Fit models
onc3_pfs_os <- as_pfs_os(onc3, patient_vars = c("patient_id", "female",
"strategy_name"))
fit_pfs <- coxph(Surv(pfs_time, pfs_status) ~ strategy_name + female,
data = onc3_pfs_os)
fit_os <- coxph(Surv(os_time, pfs_status) ~ strategy_name + female,
data = onc3_pfs_os)

## Predict survival on input data
surv_input_data <- expand(hesim_dat)
times <- seq(0, 14, 1/12)
predict_survival <- function(object, newdata, times) {
surv <- summary(survfit(object, newdata = newdata, se.fit = FALSE),
t = times)
pred <- newdata[rep(seq_len(nrow(newdata)), each = length(times)), ]
pred[, sample := 1] # Point estimates only in this example
pred[, time := rep(surv$time, times = nrow(newdata))] pred[, survival := c(surv$surv)]
return(pred[, ])
}
pfs <- predict_survival(fit_pfs, newdata = surv_input_data, times = times)
os <- predict_survival(fit_os, newdata = surv_input_data, times = times)
surv <- rbind(
as.data.table(pfs)[, curve := 1L],
as.data.table(os)[, curve := 2L]
)

## Convert predictions to a survival object
surv <- survival(surv, t = "time")
if (FALSE) autoplot(surv)

# (2) We can then compute state probabilities from the survival object
stprobs <- sim_stateprobs(surv)

# (3) Finally, we can use the state probabilities to compute QALYs and costs
## A dummy utility model to illustrate
utility_tbl <- stateval_tbl(
data.table(state_id = 1:2,
est = c(1, 1)
),
dist = "fixed"
)
utilitymod <- create_StateVals(utility_tbl,
hesim_data = hesim_dat,
n = 1)

## Instantiate Psm class and compute QALYs
psm <- Psm$new(utility_model = utilitymod) psm$stateprobs_ <- stprobs
psm$sim_qalys() psm$qalys_
#>     sample strategy_id patient_id grp_id state_id   dr    qalys      lys
#>  1:      1           1          1      1        1 0.03 4.874007 4.874007
#>  2:      1           1          1      1        2 0.03 3.070008 3.070008
#>  3:      1           1          2      1        1 0.03 4.267061 4.267061
#>  4:      1           1          2      1        2 0.03 3.166468 3.166468
#>  5:      1           2          1      1        1 0.03 5.640397 5.640397
#>  6:      1           2          1      1        2 0.03 2.605955 2.605955
#>  7:      1           2          2      1        1 0.03 4.961563 4.961563
#>  8:      1           2          2      1        2 0.03 2.789254 2.789254
#>  9:      1           3          1      1        1 0.03 6.141992 6.141992
#> 10:      1           3          1      1        2 0.03 2.399936 2.399936
#> 11:      1           3          2      1        1 0.03 5.426923 5.426923
#> 12:      1           3          2      1        2 0.03 2.639381 2.639381