*! version 1.0.0

Title

survsim -- Simulate survival data from a parametric distribution,
a user-defined distribution, from a fitted merlin model, from a cause-specific
hazards competing risks model, or from a general multi-state model

Description

survsim simulates survival data from:

help survsim parametric - a parametric distribution including the exponential, Gompertz and Weibull,
and 2-component mixtures of them. Baseline covariates can be included, with specified associated log hazard ratios.
Non-proportional hazards can also be included with all models; under an exponential or Weibull model covariates are interacted
with log time, under a Gompertz model covariates are interacted with time. See Crowther and Lambert (2012)
for more details.

help survsim user - a user-defined distribution. Survival times can be simulated from bespoke,
user-defined [log] [cumulative] hazard functions. The function must be specified in Mata code (using colon operators),
with survival times generated using a combination of numerical integration and root finding techniques. Time-dependent
effects can also be specified with a user-defined function of time. See Crowther and Lambert (2013)
for more details.

help survsim model - a fitted merlin model. merlin fits a broad class of survival models,
including standard parametric models, spline-based survival models, and user-defined survival models.

help survsim msm - a competing risks or general multi-state model. Event times can be simulated from
transition-specific hazards, where each transition hazard function can be a standard parametric distribution, or a
user-defined complex hazard function. Covariates and time-dependent effects can be specified for each transition-specific
hazard independently.

Author

Michael J. Crowther

Red Door Analytics

E-mail: michael@reddooranalytics.se

Please report any errors you may find.

References

Bender R, Augustin T and Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine 2005;24:1713-1723.

Beyersmann J, Latouche A, Buchholz A and Schumacher M. Simulating competing risks data in survival analysis. Statistics in Medicine 2009;28:956-971.

Crowther MJ and Lambert PC. Simulating complex survival data. The Stata Journal 2012;12(4):674-687.

Crowther MJ and Lambert PC. Simulating biologically plausible complex survival data. Statistics in Medicine 2013;32(23):4118-4134.

Crowther MJ. Simulating time-to-event data from parametric distributions, custom distributions,
competing risk models and general multi-state models. The Stata Journal 2022 (Accepted).

Jann, B. 2005. moremata: Stata module (Mata) to provide various functions. Available from http://ideas.repec.org/c/boc/bocode/s455001.html.