The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

A Case Study Using the Beta-Danish Distribution

Introduction

This vignette demonstrates a typical survival analysis workflow using the BetaDanish package.

library(BetaDanish)
library(survival)
#> Warning: package 'survival' was built under R version 4.5.3
#> 
#> Attaching package: 'survival'
#> The following objects are masked from 'package:BetaDanish':
#> 
#>     leukemia, transplant
data('remission', package = 'BetaDanish')
head(remission)
#>   time status
#> 1 0.08      1
#> 2 2.09      1
#> 3 3.48      1
#> 4 4.87      1
#> 5 6.94      1
#> 6 8.66      1

Fitting the Beta-Danish model

fit <- fit_betadanish(Surv(time, status) ~ 1, data = remission, n_starts = 1)
summary(fit)
#> 
#> Call:
#> fit_betadanish(formula = Surv(time, status) ~ 1, data = remission, 
#>     n_starts = 1)
#> 
#> Beta-Danish Distribution Fit
#> Model: Full 4-Parameter Model 
#> 
#>    Estimate Std. Error Lower 95% Upper 95% z value Pr(>|z|)   
#> a  0.686576   0.795132 -0.871883  2.245035  0.8635 0.387877   
#> b  4.078205   1.478893  1.179575  6.976834  2.7576 0.005823 **
#> c  2.196505   2.576019 -2.852492  7.245502  0.8527 0.393840   
#> k  0.082973   0.076769 -0.067495  0.233441  1.0808 0.279782   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Log-Likelihood: -409.9137 
#> AIC: 827.8274  | BIC: 839.2356

Three-parameter submodel

fit_sub <- fit_betadanish(Surv(time, status) ~ 1, data = remission, submodel = TRUE, n_starts = 1)
compare_models(fit, fit_sub)
#> Likelihood Ratio Test (a = 1 vs a != 1)
#> 
#>                  Model    LogLik      Chisq Df Pr(>Chisq)
#> 1   Submodel (3-param) -409.9541         NA NA         NA
#> 2 Full Model (4-param) -409.9137 0.08081129  1   0.776201

Diagnostic plots

plot(fit, type = 'survival')

plot(fit, type = 'hazard')

Interpretation

The fitted model can be used to estimate survival probabilities, hazard behavior, and overall model fit. Users should compare the Beta-Danish model with alternative lifetime distributions and inspect diagnostic plots before drawing final conclusions.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.