CohortDtstmTrans | R Documentation |
Simulate health state transitions in a cohort discrete time state transition model.
An R6::R6Class object.
params
Parameters for simulating health state transitions.
Supports objects of class tparams_transprobs
or params_mlogit_list
.
input_data
An object of class input_mats
.
cycle_length
The length of a model cycle in terms of years.
The default is 1
meaning that model cycles are 1 year long.
absorbing
A numeric vector denoting the states that are
absorbing states; i.e., states that cannot be transitioned from.
Each element should correspond to a state_id
,
which should, in turn, be the index of the health state.
start_stateprobs
A non-negative vector with length equal to the number of
health states containing the probability that the cohort is in each health
state at the start of the simulation. For example,
if there were three states and the cohort began the simulation in state 1,
then start_stateprobs = c(1, 0, 0)
. Automatically normalized to sum to 1.
If NULL
, then a vector with the first element equal to 1 and
all remaining elements equal to 0.
trans_mat
A transition matrix describing the states and transitions
in a discrete-time multi-state model. Only required if the model is
parameterized using multinomial logistic regression. The (i,j)
element
represents a transition from state i
to state j
. Each possible transition
from row i
should be based on a separate multinomial logistic regression
and ordered from 0
to K - 1
where K
is the number of
possible transitions. Transitions that are not possible should be NA
.
and the reference category for each row should be 0
.
new()
Create a new CohortDtstmTrans
object.
CohortDtstmTrans$new( params, input_data = NULL, trans_mat = NULL, start_stateprobs = NULL, cycle_length = 1, absorbing = NULL )
params
The params
field.
input_data
The input_data
field.
trans_mat
The trans_mat
field.
start_stateprobs
The start_stateprobs
field.
cycle_length
The cycle_length
field.
absorbing
The absorbing
field. If NULL
, then the constructor
will determine which states are absorbing automatically; non NULL
values
will override this behavior.
A new CohortDtstmTrans
object.
sim_stateprobs()
Simulate probability of being in each health state during each model cycle.
CohortDtstmTrans$sim_stateprobs(n_cycles)
n_cycles
The number of model cycles to simulate the model for.
An object of class stateprobs
.
clone()
The objects of this class are cloneable with this method.
CohortDtstmTrans$clone(deep = FALSE)
deep
Whether to make a deep clone.
create_CohortDtstmTrans()
creates a CohortDtstmTrans
object from either
a fitted statistical model or a parameter object. A complete economic model can be implemented
with the CohortDtstm
class.
library("msm")
library("data.table")
set.seed(101)
# We consider two examples that have the same treatment strategies and patients.
# One model is parameterized by fitting a multi-state model with the "msm"
# package; in the second model, the parameters are entered "manually" with
# a "params_mlogit_list" object.
# MODEL SETUP
strategies <- data.table(
strategy_id = c(1, 2, 3),
strategy_name = c("SOC", "New 1", "New 2")
)
patients <- data.table(patient_id = 1:2)
hesim_dat <- hesim_data(
strategies = strategies,
patients = patients
)
# EXAMPLE #1: msm
## Fit multi-state model with panel data via msm
qinit <- rbind(
c(0, 0.28163, 0.01239),
c(0, 0, 0.10204),
c(0, 0, 0)
)
fit <- msm(state_id ~ time, subject = patient_id,
data = onc3p[patient_id %in% sample(patient_id, 100)],
covariates = list("1-2" =~ strategy_name),
qmatrix = qinit)
## Simulation model
transmod_data <- expand(hesim_dat)
transmod <- create_CohortDtstmTrans(fit,
input_data = transmod_data,
cycle_length = 1/2,
fixedpars = 2,
n = 2)
transmod$sim_stateprobs(n_cycles = 2)
# EXAMPLE #2: params_mlogit_list
## Input data
transmod_data[, intercept := 1]
transmod_data[, new1 := ifelse(strategy_name == "New 1", 1, 0)]
transmod_data[, new2 := ifelse(strategy_name == "New 2", 1, 0)]
## Parameters
n <- 10
transmod_params <- params_mlogit_list(
## Transitions from stable state (stable -> progression, stable -> death)
stable = params_mlogit(
coefs = list(
progression = data.frame(
intercept = rnorm(n, -0.65, .1),
new1 = rnorm(n, log(.8), .02),
new2 = rnorm(n, log(.7, .02))
),
death = data.frame(
intercept = rnorm(n, -3.75, .1),
new1 = rep(0, n),
new2 = rep(0, n)
)
)
),
## Transition from progression state (progression -> death)
progression = params_mlogit(
coefs = list(
death = data.frame(
intercept = rnorm(n, 2.45, .1),
new1 = rep(0, n),
new2 = rep(0, n)
)
)
)
)
transmod_params
## Simulation model
tmat <- rbind(c(0, 1, 2),
c(NA, 0, 1),
c(NA, NA, NA))
transmod <- create_CohortDtstmTrans(transmod_params,
input_data = transmod_data,
trans_mat = tmat, cycle_length = 1)
transmod$sim_stateprobs(n_cycles = 2)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.