Psm | R Documentation |
Simulate outcomes from an N-state partitioned survival model.
An R6::R6Class object.
survival_models
The survival models used to predict survival curves. Must be
an object of class PsmCurves
.
utility_model
The model for health state utility. Must be an object of
class StateVals
.
cost_models
The models used to predict costs by health state.
Must be a list of objects of class StateVals
, where each element of the
list represents a different cost category.
n_states
Number of states in the partitioned survival model.
t_
A numeric vector of times at which survival curves were predicted. Determined
by the argument t
in $sim_curves()
.
survival_
An object of class survival simulated using sim_survival()
.
stateprobs_
An object of class stateprobs simulated using $sim_stateprobs()
.
qalys_
An object of class qalys simulated using $sim_qalys()
.
costs_
An object of class costs simulated using $sim_costs()
.
new()
Create a new Psm
object.
Psm$new(survival_models = NULL, utility_model = NULL, cost_models = NULL)
survival_models
The survival_models
field.
utility_model
The utility_model
field.
cost_models
The cost_models
field.
n_states
is set equal to the number of survival models plus one.
A new Psm
object.
sim_survival()
Simulate survival curves as a function of time using PsmCurves$survival()
.
Psm$sim_survival(t)
t
A numeric vector of times. The first element must be 0
.
An instance of self
with simulated output from PsmCurves$survival()
stored in survival_
.
sim_stateprobs()
Simulate health state probabilities from survival_
using a partitioned
survival analysis.
Psm$sim_stateprobs()
An instance of self
with simulated output of class stateprobs
stored in stateprobs_
.
sim_qalys()
Simulate quality-adjusted life-years (QALYs) as a function of stateprobs_
and
utility_model
. See sim_qalys()
for details.
Psm$sim_qalys( dr = 0.03, integrate_method = c("trapz", "riemann_left", "riemann_right"), lys = TRUE )
dr
Discount rate.
integrate_method
Method used to integrate state values when computing
costs or QALYs. Options are trapz
for the trapezoid rule,
riemann_left
for a left Riemann sum, and
riemann_right
for a right Riemann sum.
lys
If TRUE
, then life-years are simulated in addition to QALYs.
An instance of self
with simulated output of class qalys stored
in qalys_
.
sim_costs()
Simulate costs as a function of stateprobs_
and cost_models
.
See sim_costs()
for details.
Psm$sim_costs( dr = 0.03, integrate_method = c("trapz", "riemann_left", "riemann_right") )
dr
Discount rate.
integrate_method
Method used to integrate state values when computing
costs or QALYs. Options are trapz
for the trapezoid rule,
riemann_left
for a left Riemann sum, and
riemann_right
for a right Riemann sum.
An instance of self
with simulated output of class costs stored
in costs_
.
summarize()
Summarize costs and QALYs so that cost-effectiveness analysis can be performed.
See summarize_ce()
.
Psm$summarize(by_grp = FALSE)
by_grp
If TRUE
, then costs and QALYs are computed by subgroup. If
FALSE
, then costs and QALYs are aggregated across all patients (and subgroups).
clone()
The objects of this class are cloneable with this method.
Psm$clone(deep = FALSE)
deep
Whether to make a deep clone.
Incerti and Jansen (2021). See Section 2.3 for a mathematical description of a PSM and Section 4.2 for an example in oncology. The mathematical approach used to simulate costs and QALYs from state probabilities is described in Section 2.1.
The PsmCurves
documentation
describes the class for the survival models and the StateVals
documentation
describes the class for the cost and utility models. A PsmCurves
object is typically created using create_PsmCurves()
.
The PsmCurves
documentation provides an example in which the model
is parameterized from parameter objects (i.e., without having the patient-level
data required to fit a model with R
). A longer example is provided in
vignette("psm")
.
library("flexsurv")
library("ggplot2")
theme_set(theme_bw())
# Model setup
strategies <- data.frame(strategy_id = c(1, 2, 3),
strategy_name = paste0("Strategy ", 1:3))
patients <- data.frame(patient_id = seq(1, 3),
age = c(45, 50, 60),
female = c(0, 0, 1))
states <- data.frame(state_id = seq(1, 3),
state_name = paste0("State ", seq(1, 3)))
hesim_dat <- hesim_data(strategies = strategies,
patients = patients,
states = states)
labs <- c(
get_labels(hesim_dat),
list(curve = c("Endpoint 1" = 1,
"Endpoint 2" = 2,
"Endpoint 3" = 3))
)
n_samples <- 2
# Survival models
surv_est_data <- psm4_exdata$survival
fit1 <- flexsurvreg(Surv(endpoint1_time, endpoint1_status) ~ factor(strategy_id),
data = surv_est_data, dist = "exp")
fit2 <- flexsurvreg(Surv(endpoint2_time, endpoint2_status) ~ factor(strategy_id),
data = surv_est_data, dist = "exp")
fit3 <- flexsurvreg(Surv(endpoint3_time, endpoint3_status) ~ factor(strategy_id),
data = surv_est_data, dist = "exp")
fits <- flexsurvreg_list(fit1, fit2, fit3)
surv_input_data <- expand(hesim_dat, by = c("strategies", "patients"))
psm_curves <- create_PsmCurves(fits, input_data = surv_input_data,
uncertainty = "bootstrap", est_data = surv_est_data,
n = n_samples)
# Cost model(s)
cost_input_data <- expand(hesim_dat, by = c("strategies", "patients", "states"))
fit_costs_medical <- lm(costs ~ female + state_name,
data = psm4_exdata$costs$medical)
psm_costs_medical <- create_StateVals(fit_costs_medical,
input_data = cost_input_data,
n = n_samples)
# Utility model
utility_tbl <- stateval_tbl(tbl = data.frame(state_id = states$state_id,
min = psm4_exdata$utility$lower,
max = psm4_exdata$utility$upper),
dist = "unif")
psm_utility <- create_StateVals(utility_tbl, n = n_samples,
hesim_data = hesim_dat)
# Partitioned survival decision model
psm <- Psm$new(survival_models = psm_curves,
utility_model = psm_utility,
cost_models = list(medical = psm_costs_medical))
psm$sim_survival(t = seq(0, 5, 1/12))
autoplot(psm$survival_, labels = labs, ci = FALSE, ci_style = "ribbon")
psm$sim_stateprobs()
autoplot(psm$stateprobs_, labels = labs)
psm$sim_costs(dr = .03)
head(psm$costs_)
head(psm$sim_qalys(dr = .03)$qalys_)
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