Summarize N-1 survival curves for an N-state partitioned survival model.
An R6::R6Class object.
PsmCurves are conveniently created from either fitted models or
parameter objects with create_PsmCurves(). A complete economic model can be
implemented with the Psm class. A longer example is provided in
vignette("psm").
paramsAn object of class params_surv_list.
input_dataAn object of class input_mats. Each row in X must
be a unique treatment strategy and patient.
cumhazard()Predict the cumulative hazard function for each survival curve as a function of time.
survival()Predict survival probabilities for each survival curve as a function of time.
An object of class survival.
rmst()Predict the restricted mean survival time up until time points t
for each survival curve.
quantile()Predict quantiles of the survival distribution for each survival curve.
check()Input validation for class. Checks that fields are the correct type.
library("flexsurv")
N_SAMPLES <- 5 # Number of parameter samples for PSA
# Consider a 3-state model where there is a
# progression-free survival (PFS) and an
# overall survival (OS) endpoint
# (0) Model setup
hesim_dat <- hesim_data(
strategies = data.frame(
strategy_id = c(1, 2),
strategy_name = c("SOC", "New 1")
),
patients = data.frame(
patient_id = 1
)
)
# (1) Parameterize survival models
## (1.1) If patient-level data is available,
## we can fit survival models
### (1.1.1) Data for estimation (for simplicity, only use 2 strategies)
surv_est_data <- as_pfs_os(
onc3[strategy_name != "New 2"],
patient_vars = c("patient_id", "strategy_name")
)
surv_est_data$strategy_name <- droplevels(surv_est_data$strategy_name)
### (1.1.2) Fit models
fit_pfs <- flexsurvreg(Surv(pfs_time, pfs_status) ~ strategy_name,
data = surv_est_data, dist = "exp")
fit_os <- flexsurvreg(Surv(os_time, os_status) ~ strategy_name,
data = surv_est_data, dist = "exp")
fits <- flexsurvreg_list(pfs = fit_pfs, os = fit_os)
## (1.2) If patient-level data is NOT available,
## we can construct the parameter objects "manually"
### (1.2.1) Baseline hazard:
### Assume that we know the (log) rate parameters for both PFS and OS
### for SOC (i.e., the intercept) and their standard error
logint_pfs_est <- -1.7470900
logint_pfs_se <- 0.03866223
logint_os_est <- -2.7487675
logint_os_se <- 0.04845015
### (1.2.2) Relative treatment effect:
### Assume we know the log hazard ratios (and their standard errors)
### for comparing the new interventions to the SOC
loghr_pfs_est_new1 <- -0.1772028
loghr_pfs_se_new1 <- 0.05420119
loghr_os_est_new1 <- -0.1603632
loghr_os_se_new1 <- 0.06948962
### (1.2.3) Create "params_surv_list" object by combining the baseline hazard
### and relative treatment effects
params <- params_surv_list(
#### Model for PFS
pfs = params_surv(
coefs = list(
rate = data.frame( # coefficients predict log rate
intercept = rnorm(N_SAMPLES, logint_pfs_est, logint_pfs_se),
new1 = rnorm(N_SAMPLES, loghr_pfs_est_new1, loghr_pfs_se_new1)
)
),
dist = "exp"
),
#### Model for OS
os = params_surv(
coefs = list(
rate = data.frame(
intercept = rnorm(N_SAMPLES, logint_os_est, logint_os_se),
new1 = rnorm(N_SAMPLES, loghr_os_est_new1, loghr_os_se_new1)
)
),
dist = "exp"
)
)
#### The print (and summary) methods for the "params_surv_list" object will
#### summarize each of the model terms, which is a good way to check
#### if it's been setup correctly
params
#> A "params_surv_list" object
#>
#> Summary of coefficients:
#> model parameter term mean sd 2.5% 97.5%
#> <char> <char> <char> <num> <num> <num> <num>
#> 1: pfs rate intercept -1.7485021 0.03947666 -1.7864545 -1.69543149
#> 2: pfs rate new1 -0.1283063 0.05808532 -0.2164273 -0.08005826
#> 3: os rate intercept -2.7452407 0.03625975 -2.7783701 -2.69718181
#> 4: os rate new1 -0.1333935 0.04073142 -0.1598637 -0.06980461
#>
#> Number of parameter samples: 5
#> Distributions:
#> pfs os
#> "exp" "exp"
# (2) Simulation
## (2.1) Construct the model
### (2.1.1) Case where patient-level data was available
### Use create_PsmCurves.params_flexsurvreg_list() method
surv_input_data <- expand(hesim_dat, by = c("strategies", "patients"))
psm_curves1 <- create_PsmCurves(fits, input_data = surv_input_data,
n = N_SAMPLES,
uncertainty = "normal",
est_data = surv_est_data)
### (2.1.2) Case where patient-level data was NOT available
### Use create_PsmCurves.params_surv_list() method
surv_input_data$intercept <- 1
surv_input_data$new1 <- ifelse(surv_input_data$strategy_name == "New 1",
1, 0)
psm_curves2 <- create_PsmCurves(params, input_data = surv_input_data)
## (2.2) Summarize survival models
## There are minor discrepancies between the case where models were fit
## with flexsurvreg() and the case where the "params_surv_list" object
## was constructed manually due to differences in the random draws
## of the parameter samples. These differences are decreasing in the size
## of N_SAMPLES
times <- seq(0, 10, 1/12) # Monthly times
### Quantiles
head(psm_curves1$quantile(p = c(.25, .5, .75)))
#> sample strategy_id patient_id grp_id curve p quantile
#> <num> <int> <int> <int> <num> <num> <num>
#> 1: 1 1 1 1 1 0.25 1.576211
#> 2: 1 1 1 1 1 0.50 3.797756
#> 3: 1 1 1 1 1 0.75 7.595513
#> 4: 1 1 1 1 2 0.25 4.644797
#> 5: 1 1 1 1 2 0.50 11.191272
#> 6: 1 1 1 1 2 0.75 22.382543
head(psm_curves2$quantile(p = c(.25, .5, .75)))
#> sample strategy_id patient_id grp_id curve p quantile
#> <num> <int> <int> <int> <num> <num> <num>
#> 1: 1 1 1 1 1 0.25 1.701678
#> 2: 1 1 1 1 1 0.50 4.100060
#> 3: 1 1 1 1 1 0.75 8.200119
#> 4: 1 1 1 1 2 0.25 4.629897
#> 5: 1 1 1 1 2 0.50 11.155370
#> 6: 1 1 1 1 2 0.75 22.310739
### Survival curves
head(psm_curves1$survival(t = times))
#> sample strategy_id patient_id grp_id curve t survival
#> <num> <int> <int> <int> <num> <num> <num>
#> 1: 1 1 1 1 1 0.00000000 1.0000000
#> 2: 1 1 1 1 1 0.08333333 0.9849055
#> 3: 1 1 1 1 1 0.16666667 0.9700389
#> 4: 1 1 1 1 1 0.25000000 0.9553966
#> 5: 1 1 1 1 1 0.33333333 0.9409754
#> 6: 1 1 1 1 1 0.41666667 0.9267718
head(psm_curves2$survival(t = times))
#> sample strategy_id patient_id grp_id curve t survival
#> <num> <int> <int> <int> <num> <num> <num>
#> 1: 1 1 1 1 1 0.00000000 1.0000000
#> 2: 1 1 1 1 1 0.08333333 0.9860106
#> 3: 1 1 1 1 1 0.16666667 0.9722169
#> 4: 1 1 1 1 1 0.25000000 0.9586162
#> 5: 1 1 1 1 1 0.33333333 0.9452058
#> 6: 1 1 1 1 1 0.41666667 0.9319829
### Restricted mean survival
head(psm_curves1$rmst(t = c(2, 5)))
#> sample strategy_id patient_id grp_id curve t rmst
#> <num> <int> <int> <int> <num> <num> <num>
#> 1: 1 1 1 1 1 2 1.675611
#> 2: 1 1 1 1 1 5 3.279243
#> 3: 1 1 1 1 2 2 1.881087
#> 4: 1 1 1 1 2 5 4.299891
#> 5: 1 2 1 1 1 2 1.731940
#> 6: 1 2 1 1 1 5 3.536659
head(psm_curves2$rmst(t = c(2, 5)))
#> sample strategy_id patient_id grp_id curve t rmst
#> <num> <int> <int> <int> <num> <num> <num>
#> 1: 1 1 1 1 1 2 1.696977
#> 2: 1 1 1 1 1 5 3.374980
#> 3: 1 1 1 1 2 2 1.880720
#> 4: 1 1 1 1 2 5 4.297859
#> 5: 1 2 1 1 1 2 1.752854
#> 6: 1 2 1 1 1 5 3.636406