NEWS.md
A survival
object can now be constructed manually with survival()
and simulated state probabilities can be computed from survival curves with sim_stateprobs.survival()
. These features are useful for partitioned survival analyses when a user would like to use a survival model not supported by hesim
to predict survival curves (#49).
tpmatrix()
is more flexible:
There is now a complement
argument where users can define transitions that are “complements”. This is particularly helpful for creating large transition probability matrices since there is no longer a need to manually enter "C"
in the ...
portion of tpmatrix()
. In other words, users can pass a single object to tpmatrix()
and use the complement
argument to ensure probabilities sum to 1 in each row. One reasonable workflow with define_tparams()
would be to (i) create a single matrix of initial values (say zeros), (ii) modify the transitions that differ from the initial values, and (iii) pass the resulting object to tpmatrix()
while using the complement
argument.
The states
, prefix
, and sep
arguments can be used to name the columns (i.e., the transitions) and the states. The named states are, in turn, passed to the $value
element of a tparams_transprobs
object with tparams_transprobs.tpmatrix()
.
An eval_rng()
object can now be passed directly to define_model()
, meaning that users can sample parameters prior to defining a model. Previously users could only pass an rng_def
object to define_model()
, which meant that sampling could only occur while evaluating a model_def
object.
There are now summary and print methods for parameter, transformed parameter, and eval_rng
objects. See summary.params()
, summary.tparams_transprobs()
, summary.tparams_mean()
, and summary.eval_rng()
.
An input_mats
object can be converted to a data.table
with as.data.table.input_mats()
and printed to the console in a less verbose way than in prior versions with print.input_mats()
.
sim_ev()
, sim_costs()
, and sim_qalys()
are now exported functions that give users additional flexibility in their modeling pipelines and provide improved documentation for computation of expected values in cohort models. sim_ev()
is particularly useful for computing outcomes that depend on time in state other than costs or quality-adjusted life-years (QALYs).
Multiple absorbing states (or none at all) are possible in hesim::CohortDtstm
and hesim::IndivCtstm
models (previously the final health state was always assumed to be a death state). In cohort models, the absorbing states can be set manually using the absorbing
field in the hesim::CohortDtstmTrans
class; if not, they are set automatically based on the transition probabilities. The number of health states in state value models (class hesim::StateVals
) must equal the number of health states in the transition models less the number of absorbing states.
A new create_CohortDtstmTrans.params_mlogit_list()
method allows the transition component of a cohort discrete time state transition model (cDTSTM) to be created directly from multinomial logistic regression parameter objects.
The coefficient elements of parameter objects can be constructed from any object (e.g., data frame) than can be passed to as.matrix()
(rather than only from matrices as in previous versions). See, for instance, params_surv()
.
sim_stateprobs.survival()
handles scenarios where survival curves cross better, ensuring that state probabilities sum to 1 (#56).A transition intensity matrix can be created from a multi-state model fit using msm::msm()
with the qmatrix.msm()
method. Similarly, a CohortDtstmTrans
object can be created with create_CohortDtstmTrans.msm()
.
The ...
argument to create_PsmCurves()
now passes arguments to create_params.partsurvfit()
when object
is of class flexsurvreg_list
. This allows more control over bootstrapping (i.e., use of the max_errors
and silent
arguments).
summary.ce()
is a new summary method for a hesim::ce
object that produces confidence intervals for QALYs and each cost category; format.summary.ce()
formats the output for pretty printing.
icer()
generates a tidy table of incremental cost-effectiveness ratios (ICERs) given output from cea_pw()
; format.icer()
formats the output for pretty printing.
plot_ceplane()
, plot_ceac()
, plot_ceaf()
, and plot_evpi()
plot the cost-effectiveness plane, cost-effectiveness acceptability curve (CEAC), cost-effectiveness acceptability frontier (CEAF), and expected value of perfect information (EVPI), respectively.
autoplot.survival()
and autoplot.stateprobs()
plot survival curves and state probabilities, respectively.
The first column of each matrix listed in the coef
element returned by create_params.flexsurvreg()
is now named “(Intercept)” instead of the name of the corresponding parameter.
The create_params()
methods now use the argument uncertainty
to draw parameters and the old arguments point_estimate
and bootstrap
are deprecated. This also affects create_CohortDtstmTrans()
, create_IndivCtstmTrans()
, and create_PsmCurves()
.
icer_tbl()
has been deprecated in favor of icer()
.
The column trans
in the data table returned by the $hazard()
and $cumhazard()
methods from the hesim::CtstmTrans
class has been renamed transition_id
.
$summarize()
method of hesim::Psm
now contains the by_grp
argument so that subgroup analyses can be performed.There are new functions to construct (and debug the construction of) the multiple transition probability matrices stored in tparams_transprobs()
objects and used for cDTSTMs. These can either stored as 3-dimensional arrays or as 2-dimensional tabular objects (i.e., data.table
, data.frame
, matrix
).
as_array3()
and as_tbl2()
lets users convert 2-dimensional tabular objects where each row stores a flattened square matrix to a 3-dimensional array of square matrices and vice versa.
qmatrix()
lets you store transition intensity matrices which can be used to construct transition probability matrices with the matrix exponential via expmat()
. The latter is a simple wrapper around msm::MatrixExp()
that computes the matrix exponential for all matrices in an array rather than just a single matrix.
apply_rr()
applies relative risks (stored in a 2-dimensional tabular object) to (potentially multiple elements) of transition probability matrices stored in an array. This function is vectorized so it can be performed very quickly even for large arrays.
as.data.table.tparams_transprobs()
converts the array of transition probability matrices stored in a tparams_transprobs
object into a data.table
which can be helpful for debugging to ensure that the right transition probability matrices correspond to the right observations (i.e., treatment strategies, patients, etc.).
The tpmatrix
element of define_tparams()
can now be a 3-dimensional array in addition to the output of tpmatrix()
to increase flexibility for the user.
2-dimensional tabular objects (in addition to vectors) can now be passed to ...
with tpmatrix()
. See the new examples.
A new dataset hesim::onc3
was added as an example multi-state dataset for an oncology use case with 3 health states (Stable, Progression, Death) and 3 possible transitions (Stable -> Progression, Stable -> Death, and Progression -> Death). This is similar to hesim::mstate3_exdata
but does not allow for reversible transitions and does not contain cost or utility data.
The function as_pfs_os()
can convert a multi-state dataset in the same format as hesim::onc3
into a dataset with one row per patients containing time to event information for progression free survival (PFS) and overall survival (OS).
The cycle_length
field in CohortDtstmTrans
was fixed so that it corresponds to a model cycle in terms of years (e.g., a value of 2 means a model cycle is 2 years long and that state probabilities are computed every 2 years with $sim_stateprobs()
).
The simulated dataset multinom3_exdata
was fixed by removing a bug where some patients were simulated to have died more than once.
Fixed bug where the $sim_costs()
method of IndivCtstm
was erroneously returning a life-years column in addition to the costs column.
Modification to creation of input matrices from a flexsurvreg
object to properly capture levels of factor variables.
Minor updates to the documentation and fixes to small problems in the C++
code identified with the CRAN package checks.
IndivCtstmTrans
objects can be constructed from a params_surv_list
using create_IndivCtstmTrans.params_surv_list()
.
Survival models can randomly sample from piecewise exponential and proportional hazards Weibull distributions. A fixed
distribution has also been added so that survival times can be set to a single constant value. Random number generation from truncated versions of these distributions is also supported. Note that functionality beyond random number generation (e.g., hazard functions, cumulative hazard functions, cumulative density functions) is not yet complete. See ?params_surv
.
A new vignette incorporates the two bullets above and shows how a time-inhomogeneous Markov model can be simulated using individual patient simulation.
Disease progression (i.e., a trajectory through a multi-state model) can be simulated using the sim_disease()
method of the hesim::IndivCtstmTrans
class.
A more computationally efficient approach to simulation of time-inhomogeneous Markov cohort models has been added to the corresponding vignette. This was aided by the new tpmatrix_id()
and tparams_transprobs.tpmatrix()
functions.
The “Articles” on the package website have been reorganized so that they align more closely with the different types of economic models.
The lys
argument for the $sim_qalys()
method of hesim::Psm
and hesim::CohortDtstm
classes now works.
The $sim_stateprobs()
argument for the hesim::Psm
class now properly returns the patient_wt
column.
hesim
now supports cDTSTM via hesim::CohortDtstm
objects. Users can build a model by either fitting multinomial logistic regressions with nnet::multinom()
or with a mathematical expression using define_model()
. Furthermore, $summarize()
methods now have a by_grp
option to facilitate subgroup analyses.
The hesim::CohortDtstm
class simulates cDTSTMs. State transitions in a cDTSTM are simulated using the hesim::CohortDtstmTrans
class, which can be constructed from a multinom_list()
object or using define_model()
.
$summarize()
methods now have a by_grp = "TRUE"
option to facilitate subgroup analyses. If by_grp = "FALSE"
, then estimates are aggregated across groups. A new patient_wt
argument in the patients
table of hesim_data()
can be used to weight groups during the aggregation.
hesim::tparams
objects can now be used to store transformed parameters used to simulate outcomes such as means (i.e., tparams_mean()
) that have already been predicted as a function of covariates.
General cumulative hazard functions can now be simulated using a discrete time approximation where the probability of an event during each time period is simulated from a Bernoulli distribution. This is more efficient than the previous method based on a C++
version of the sample()
function. See the random_method = "discrete"
option in params_surv()
.
rdirichlet_mat()
has a new argument output
so that multiple object types can be returned.
The auxiliary argument random_method = "sample"
in params_surv()
is deprecated and random_method = "discrete"
should be used instead.
stateval_tbl
now contains a grp_var
column used to assign state values to “groups” of patients. This is distinct from grp_id
in hesim_data()
, which is used to define groups for subgroup analyses.
The public field input_mats
has been renamed input_data
in R6
classes for disease progression and state values. This is a more generic name and will allow for potential feature enhancements in which input_data
is a data frame rather than a matrix.
rdirichlet_mat()
has been modified to better facilitate sampling from transition matrices within the context of define_model()
and tparams_transprobs()
. One implication is that the number of rows in alpha
must now be less than or equal to the number of columns and that the number of columns can be greater than the number of rows.
Remove a documented ...
that was not used in weibullNMA()
.
The input_mats
class now contains an element time_reset
. If TRUE
, then time intervals reset each time a patient enters a new health state. In other words, state values can depend on time since entering a health state.
To illustrate, consider an oncology application with three health states (stable disease, progressed disease, and death). In these models it is common to assume that patients begin second line treatment after disease progression. Suppose the second line treatment is a chemotherapy that patients take for 12 cycles (or approximately 1 year). Then drug costs would accrue for the first year but not afterwards.
State values like this can be specified by setting time_reset = TRUE
in create_StateVals.stateval_tbl()
.
hesim_dat <- hesim_data(strategies = data.frame(strategy_id = c(1, 2)),
patients = data.frame(patient_id = seq(1, 3)),
states = data.frame(state_id = c(1, 2)))
drugcosts <- stateval_tbl(tbl = data.frame(state_id = rep(c(1, 2), each = 2),
time_start = c(0, 1, 0, 1),
est = c(10000, 0, 12500, 0)),
dist = "fixed",
hesim_data = hesim_dat)
drugcostsmod <- create_StateVals(drugcosts, time_reset = TRUE)
hesim
now provides a general framework for integrating statistical models with economic evaluation. Users build a decision model by specifying a model structure, which consists of a set of statistical models for disease progression, utilities, and costs. Each statistical model is used to simulate outcomes as a function of estimated parameters and input data. N-state partitioned survival models (PSMs) and individual-level continuous time state transition models (iCTSTMs) are now supported.
Economic models—which combine the disease, utility, and cost models—are constructed with the following classes:
hesim::Psm()
for PSMshesim::IndivCtstm()
for iCTSTMsDisease models are constructed using the classes:
hesim::PsmCurves
to simulate survival curves for each endpoint of interesthesim::IndivCtstmTrans
to simulate health state transitions with a iCTSTMUtility and cost models are constructed with the hesim::StateVals
class.
The economic models are used to simulate disease progression ($sim_disease()
, $sim_stateprobs()
), quality-adjusted life-years (QALYs) ($sim_qalys()
), and costs ($sim_costs()
). Parameter uncertainty is propagated to model outcomes using probabilistic sensitivity analysis. Summaries of the simulated costs and QALYs are used to perform model-based cost-effectiveness analyses (CEAs) and represent decision uncertainty with icea.ce()
and icea_pw.ce()
.
sim
was renamed sample
in icea()
, icea_pw()
, and incr_effect()
.icea()
and icea_pw()
.The initial CRAN submission containing support for CEA but not for model development. Decision uncertainty is represented using cost-effectiveness planes, cost-effectiveness acceptability curves, cost-effectiveness acceptability frontiers, and the expected value of perfect information. CEAs by subgroup (i.e., individualized CEAs) are performed with icea()
and icea_pw()
.