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Changelog

Source: NEWS.md

ertte 0.1.0

Initial CRAN release.

  • Model fitting and prediction for exposure-response models of time-to-event endpoints: ertte_aft() (a parametric accelerated failure time model built on survival::survreg(), supporting exponential, Weibull, log-normal, and log-logistic distributions) and ertte_coxph() (a semi-parametric proportional-hazards model built on survival::coxph()).
  • ertte_aft_select_distribution(), AIC-based selection across the supported AFT base distributions.
  • ertte_predict() and ertte_fun() for survival-probability prediction and counterfactual curve evaluation without refitting, for both engines.
  • ertte_landmark() and ertte_rmst(), reducing a fitted survival curve to a binary landmark response or a restricted mean survival time, with confidence intervals.
  • simulate.ertte_model(), drawing simulated event/censoring times from a fitted model, with an optional per-row administrative censor_time.
  • Stepwise covariate modelling (ertte_scm_forward(), ertte_scm_backward(), ertte_scm_history()), built on the single-term ertte_add_term()/ertte_remove_term() helpers, with a choice of "p-value", "aic", or "bic" selection criterion.
  • ertte_power(), a power-function covariate transform usable as a model term in either engine’s formula or an SCM candidate set.
  • Interoperability with the companion erplots package via er_predict()/er_simulate()/er_summary()/ er_predict_survival() methods, registered lazily so ertte has no hard dependency on erplots or on any plotting package.
  • An example dataset, ertte_data, simulated from a Weibull AFT ground truth with an exposure and sex effect, administrative censoring, and independent dropout censoring.