Package: surtvep 1.0.0

Lingfeng Luo

surtvep: Cox Non-Proportional Hazards Model with Time-Varying Coefficients

Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) <doi:10.1007/s10985-021-09544-2> and Luo et al. (2023) <doi:10.1177/09622802231181471>.

Authors:Lingfeng Luo [aut, cre], Wenbo Wu [aut], Kevin He [aut]

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surtvep.pdf |surtvep.html✨
surtvep/json (API)

# Install 'surtvep' in R:

install.packages('surtvep', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/um-kevinhe/surtvep/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

ExampleData - Example data with 2000 observations of 2 continuous variables
ExampleDataBinary - Example data with 2000 observations of 2 binary variables
StrataExample - Example data for stratified model illustration
support - Study to Understand Prognoses Preferences Outcomes and Risks of Treatment

1.70 score 2 scripts 140 downloads 9 exports 69 dependencies

Last updated 1 years agofrom:6653cdfcf2. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Oct 11 2024
R-4.5-linux-x86_64	OK	Oct 11 2024

Exports:baseline coxtp coxtv cv.coxtp get.tvcoef IC tvef.ph tvef.zero tvef.zero.time

Dependencies:abind backports boot broom car carData cli colorspace corrplot cowplot cpp11 Deriv doBy dplyr fansi farver Formula generics ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable isoband labeling lattice lifecycle lme4 magrittr MASS Matrix MatrixModels mgcv microbenchmark minqa modelr munsell nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig polynom purrr quantreg R6 RColorBrewer Rcpp RcppArmadillo RcppEigen rlang rstatix scales SparseM stringi stringr survival tibble tidyr tidyselect utf8 vctrs viridisLite withr

Help page	Topics
calculating baseline hazard and baseline cumulative hazard using the result from a 'coxtv' or 'coxtp' object	baseline
get confidence intervals of time-varying coefficients from a fitted object	confint.coxtp confint.coxtv
fit a Cox non-proportional hazards model with P-spline or Smoothing-spline, with penalization tuning parameter chosen by information criteria or cross-validation	coxtp
fit a Cox non-proportional hazards model	coxtv
fit a cross-validated Cox non-proportional hazards model with P-spline or Smoothing-spline where penalization tuning parameter is provided by cross-validation	cv.coxtp
example data with 2000 observations of 2 continuous variables	ExampleData
example data with 2000 observations of 2 binary variables	ExampleDataBinary
helper function to get time-varying coefficients	get.tvcoef
calculating information criteria from a 'coxtp' object	IC
plotting the baseline hazard	plot.baseline
plotting results from a fitted 'coxtp' object	plot.coxtp
plotting results from a fitted 'coxtv' object	plot.coxtv
example data for stratified model illustration	StrataExample
Study to Understand Prognoses Preferences Outcomes and Risks of Treatment	support
testing the proportional hazards assumption from a 'coxtv' or 'coxtp' object	tvef.ph
testing the significance of the covariates from a 'coxtv' or 'coxtp' object	tvef.zero
testing the significance of the covariates from a 'coxtv' or 'coxtp' object using a Wald test statistic	tvef.zero.time

Package: surtvep 1.0.0

surtvep: Cox Non-Proportional Hazards Model with Time-Varying Coefficients

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)