| Type: | Package |
| Title: | Competing Risk in Dependent Net Survival Analysis |
| Version: | 1.0.0 |
| Date: | 2025-01-09 |
| Description: | Provides statistical tools for analyzing net and relative survival, with a key feature of relaxing the assumption of independent censoring and incorporating the effect of dependent competing risks. It employs a copula-based methodology, specifically the Archimedean copula, to simulate data, conduct survival analysis, and offer comparisons with other methods. This approach is detailed in the work of Adatorwovor et al. (2022) <doi:10.1515/ijb-2021-0016>. |
| Encoding: | UTF-8 |
| Imports: | methods, Rcpp, Rmpfr, copula, gplots, ggplot2, viridis, sqldf,data.table, relsurv |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown, markdown |
| VignetteBuilder: | knitr |
| License: | GPL (≥ 3) |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | yes |
| Packaged: | 2025-01-19 06:03:35 UTC; Admin |
| Author: | Reuben Adatorwovor [aut], Paul Little [cre] |
| Maintainer: | Paul Little <pllittle321@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-01-21 15:40:05 UTC |
opt_sum
Description
Summarizes the solutions post-optimization
Usage
opt_sum(OPT)Arguments
OPT | Output list from |
Value
A dataframe containing a summary of each successfully optimized configuration of copula and density with correspondingconstrained maximum likelihood estimates, and Bayesian Information Criteria.
plot_SURVs
Description
Plot net survival probabilities per model
Usage
plot_SURVs(run_ANA, MULTIPLE, ncol = 1, ALPHA = 0.5)Arguments
run_ANA | The object outputted from |
MULTIPLE | A boolean set to |
ncol | Integer number of columns of plots to display |
ALPHA | A numeric value between 0 and 1 to control theconfidence band transparency. |
Value
Returns a ggplot object of survival predictions and corresponding confidence intervals, Bayesian Information Criteria,maximum likelihood estimates. The user may apply their owncustomized graphic edits prior to visualizing the final graphicwith theprint() function.
refData_match
Description
This function takes as inputa working dataset of interest and a reference dataset.
Usage
refData_match(wDAT, rDAT, ncores = 1)Arguments
wDAT | A working dataset data.frame containing required columns |
rDAT | A reference dataset data.frame containingrequired columns |
ncores | Integer number of parallel threads to decreasematching runtime. |
Value
A dataframe containing calculated log-transformed density and log-transformed cumulative distribution
run_analyses
Description
This function performs a full analysis of an inputted dataframe. The user may specify one of two copulas, atheta value, a parametric grid to search over, and a vector of times for predicting survival.
Usage
run_analyses( DATA, THETAs = NULL, upKAPPA, gTHRES = 0.1, COPULAS, param_grid, vec_time, ncores = 1, max_iter = 200, verb, PLOT)Arguments
DATA | A data.frame containing column names |
THETAs | A vector of theta values to explore and optimize over. |
upKAPPA | An integer value taking values 0 or 1. If set to 1, the exponentiated Weibull distribution is assumed. Otherwise,the Weibull distribution is assumed and optimized over. Ifundefined, the optimization will search over both distributions. |
gTHRES | A numeric threshold on the L2 norm of the gradient evaluated at the MLE. |
COPULAS | If undefined, will optimize over all copulas. Otherwise set to 'Independent', 'Clayton' or 'Gumbel' |
param_grid | Vector of values spanning possible log(alpha1), log(lambda1), log(kappa1), unconstrained theta parameters |
vec_time | Vector of times in years to calculate predicted survival. |
ncores | A positive integer for the number of threads to evaluate log-likelihoods across the parameter grid. |
max_iter | Maximum Newton Raphson and Gradient Descent iterationsto set. |
verb | Boolean value to display verbose information or not |
PLOT | A logical variable, set to |
Value
Returns a parsable list of results per successfully optimized configuration of copula and density with accompanying net survival predictions, survival confidence intervals, maximum likelihood estimates, MLE confidence intervals (constrained and unconstrained), Bayesian Information Criteria for model selection, and extra statistical metrics to confirm convergence.