ertte
Exposure-Response Tools for Time-to-Event Models
Provides estimation tools for exposure-response models of time-to-event endpoints based on the 'survival' package: parametric accelerated failure time model fitting and prediction with AIC-based distribution selection, stepwise covariate modelling, and simulation-based visual predictive checks. Tested and supported for the exponential, Weibull, log-normal, and log-logistic distributions. For a model-agnostic mini-language to visualise exposure-response models (including those fitted with 'ertte'), see the companion package 'erplots'.
ertte
Provides tools for working with exposure-response models for time-to-event data. It is designed with a similar interface to the erglm and emaxnls packages, and supports the plotting interface supplied by the erplots package. It provides two model-fitting engines, one for parametric AFT regression models and another for semi-parametric Cox proportional-hazards regression, behind a shared, tidy interface for survival-probability prediction, restricted mean survival time, landmark analysis, stepwise covariate modelling, and simulation.
Installation
You can install the development version of ertte from GitHub with:
# install.packages("pak")
pak::pak("djnavarro/ertte")
Example
library(ertte)
mod <- ertte_aft(Surv(time, event) ~ aucss, ertte_data)
mod
#> Call:
#> survival::survreg(formula = formula, data = data, dist = dist)
#>
#> Coefficients:
#> (Intercept) aucss
#> 4.8563375087 -0.0006407913
#>
#> Scale= 0.7164032
#>
#> Loglik(model)= -1207.3 Loglik(intercept only)= -1263.9
#> Chisq= 113.25 on 1 degrees of freedom, p= <2e-16
#> n= 300
ertte_predict(mod, ertte_data[1:5, ], time = c(30, 60, 90))
#> # A tibble: 15 × 14
#> id sex age weight dose treatment aucss cmaxss time event admin_censor fit_survival ci_lower ci_upper
#> <int> <fct> <int> <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Female 27 70 200 Drug 1114. 187. 30 1 180 0.701 0.666 0.733
#> 2 1 Female 27 70 200 Drug 1114. 187. 60 1 180 0.393 0.344 0.441
#> 3 1 Female 27 70 200 Drug 1114. 187. 90 1 180 0.193 0.152 0.237
#> 4 2 Female 27 59 100 Drug 561. 49.1 30 0 180 0.805 0.780 0.827
#> 5 2 Female 27 59 100 Drug 561. 49.1 60 0 180 0.565 0.521 0.607
#> 6 2 Female 27 59 100 Drug 561. 49.1 90 0 180 0.366 0.317 0.416
#> 7 3 Female 24 65 0 Placebo 0 0 30 0 180 0.877 0.856 0.895
#> 8 3 Female 24 65 0 Placebo 0 0 60 0 180 0.708 0.665 0.747
#> 9 3 Female 24 65 0 Placebo 0 0 90 0 180 0.544 0.487 0.598
#> 10 4 Female 29 63 0 Placebo 0 0 30 0 180 0.877 0.856 0.895
#> 11 4 Female 29 63 0 Placebo 0 0 60 0 180 0.708 0.665 0.747
#> 12 4 Female 29 63 0 Placebo 0 0 90 0 180 0.544 0.487 0.598
#> 13 5 Male 27 91 200 Drug 1416. 143. 30 1 180 0.628 0.584 0.669
#> 14 5 Male 27 91 200 Drug 1416. 143. 60 1 180 0.294 0.242 0.347
#> 15 5 Male 27 91 200 Drug 1416. 143. 90 1 180 0.116 0.0824 0.155