Package: simtte 1.1.0

Carlos Traynor

simtte: Simulate Time-to-Event Data from PK/PD and Bespoke ODE Models

Simulates time-to-event (survival) datasets for clinical trial design and analysis, including datasets driven by a joint pharmacokinetic/pharmacodynamic (PK/PD) model solved via the 'mrgsolve' backend. Event times can be located directly during ODE integration ('in-solver' event detection) using a library of built-in PK/PD hazard models or a user-supplied 'mrgsolve' model converted for this purpose, or generated by inverse transform sampling from a closed-form Weibull or flexible M-spline baseline hazard, or from any externally simulated survival trajectory. See Bender et al. (2005) <doi:10.1002/sim.2059> for the inverse transform sampling methodology and Royston and Parmar (2002) <doi:10.1002/sim.1203> for flexible parametric survival models.

Authors:Carlos Traynor [aut, cre]

simtte_1.1.0.tar.gz
simtte_1.1.0.zip(r-4.7-any)simtte_1.1.0.zip(r-4.6-any)simtte_1.1.0.zip(r-4.5-any)
simtte_1.1.0.tgz(r-4.6-any)simtte_1.1.0.tgz(r-4.5-any)
simtte_1.1.0.tar.gz(r-4.7-any)simtte_1.1.0.tar.gz(r-4.6-any)
simtte_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
simtte/json (API)

# Install 'simtte' in R:
install.packages('simtte', repos = c('https://csetraynor.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/csetraynor/simtte/issues

Datasets:
  • ms_data - Simulated Data for M-Splines Model Demonstration

On CRAN:

Conda:

5.63 score 1 stars 19 scripts 155 downloads 19 exports 19 dependencies

Last updated from:652c6ee925. Checks:6 OK, 3 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK165
source / vignettesOK208
linux-releaseOK158
macos-releaseOK198
macos-oldrelOK233
windows-develERROR192
windows-releaseERROR208
windows-oldrelERROR155
wasm-releaseOK108

Exports:%>%add_censoringadd_interval_censoringcensoring_rate_fordraw_censoring_timesexplore_pi_tq_survsim_ttesim_tte_dfsim_tte_odesim_tte_ode_modelssimtte_example_modelsimtte_example_modelssimtte_model_cachesimtte_model_cache_clearsimtte_prepare_modelthin_visitstte_modelvisit_schedulevisit_schedule_informative

Dependencies:BHclidplyrgenericsgluelifecyclemagrittrmrgsolvepillarpkgconfigR6RcppRcppArmadillorlangtibbletidyselectutf8vctrswithr

Bring Your Own PK/PD Model
1. A minimal conversion | 2. What conversion actually does | 3. $OMEGA coexistence | 4. The lp/hazard boundary | 5. Compile cost and caching
Rendered frombring-your-own-model.Rmdusingknitr::rmarkdown

Last update: 2026-10-18
Started: 2026-09-13

Joint PK/PD and Time-to-Event Simulation
1. What this is for | 2. Library models | 3. Covariates and the linear predictor | Where a covariate ends and a model parameter begins | Combining dosing data with covariates | 4. Between-subject variability | 5. Beyond this tour | 6. Accuracy, messages, and when things get slow | 7. Backward compatibility
Rendered frompkpd-time-to-event.Rmdusingknitr::rmarkdown

Last update: 2026-10-18
Started: 2026-09-13

Censoring and Realistic Assessment Schedules
1. Right censoring | Choosing the censoring rate for a target censoring fraction | 2. Interval censoring | Left censoring | Delayed entry (not implemented) | 3. Realistic visit schedules | Missed visits and assessment dropout | Informative assessment schedules
Rendered fromcensoring-and-assessment.Rmdusingknitr::rmarkdown

Last update: 2026-09-13
Started: 2026-09-13

Advanced Simulation Scenarios
Exploring the Prognostic Index | Visualisation | Custom mrgsolve Models with sim_tte_df | Event-Time Interpolation | Multiple Treatment Arms
Rendered fromadvanced-usage.Rmdusingknitr::rmarkdown

Last update: 2026-09-13
Started: 2026-05-15

Getting Started with simtte
Introduction | Statistical Framework | Weibull Model | M-Spline Model | Inverse Transform Sampling | Basic Workflow | Weibull Example | M-Splines Example | Event-Time Interpolation | Output Structure | References
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-09-13
Started: 2026-05-15

Readme and manuals

Help Manual

Help pageTopics
Apply independent right censoring to a simulated events data frameadd_censoring
Map simulated event/censoring times onto an assessment-time intervaladd_interval_censoring
Solve for a censoring-distribution parameter giving a target censoring fractioncensoring_rate_for
Draw independent times from a censoring/delay distribution specdraw_censoring_times
Explore Prognostic Index and Quantile Survival Timesexplore_pi_tq_surv
Simulated Data for M-Splines Model Demonstrationms_data
Print a converted PK/PD modelprint.simtte_model
Print an in-solver simulation resultprint.simtte_ode_sim
Print a prepared modelprint.simtte_prepared_model
Simulate Time-to-Event Datasim_tte
Simulate Time-to-Event Data from a Custom mrgsolve Outputsim_tte_df
Simulate Time-to-Event Data via In-Solver Event Detectionsim_tte_ode
List sim_tte_ode()'s bundled library modelssim_tte_ode_models
Load a bundled simtte example mrgsolve modelsimtte_example_model
List simtte's bundled example mrgsolve modelssimtte_example_models
Where simtte builds and caches compiled modelssimtte_model_cache
Clear simtte's compiled-model cachesimtte_model_cache_clear
Prepare a model for simulation on this process, oncesimtte_prepare_model
Thin a visit schedule with missed visits and dropout (C1)thin_visits
Convert a PK/PD mrgsolve model into a `sim_tte_ode()`-ready modeltte_model
Generate a per-subject visit schedule with fixed spacing and jittervisit_schedule
Generate an outcome-informative visit schedule (C2)visit_schedule_informative