Package: survdt 0.9.0

Omar Vazquez

survdt: Improved Methods for Survival Analysis under Double Truncation

Contains existing and novel methods for nonparametric analysis and Cox regression analysis with doubly truncated survival data, as described in Vazquez and Xie (2025) <doi:10.1007/s10985-025-09650-5>. Includes survival curves and hazard estimates through nonparametric maximum likelihood estimation, various tests for detecting group differences or non-ignorable sampling bias, and inverse probability weighted Cox regression with several options of nonparametric weights. Also implements diagnostics for key modeling assumptions such as quasi-independent truncation and the positivity assumption. Closed-form standard errors are available for all estimates, i.e. bootstrapping is not required.

Authors:Omar Vazquez [aut, cre, cph], Sharon X. Xie [ths]

survdt_0.9.0.tar.gz
survdt_0.9.0.tar.gz(r-4.7-any)survdt_0.9.0.tar.gz(r-4.6-any)
survdt_0.9.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
survdt/json (API)

# Install 'survdt' in R:
install.packages('survdt', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • aids - AIDS incubation data
  • nonprop_sample - Simulated data with non-proportional hazards and independent double truncation

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.30 score 444 downloads 15 exports 23 dependencies

Last updated from:a06e20567d. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK133
source / vignettesOK188
linux-release-x86_64OK133
wasm-releaseOK165

Exports:coxph_indtruncis.npmleis.Survdtis.Survdt2npmleplot_coxsurvpositivity_sens_indtruncSurvdtSurvdt2survdt2to1test_ignorability_indtrunctest_quasiindep_covariatestest_quasiindep_ctautest_samplediff_indtrunctime_split

Dependencies:clicpp11farverggplot2gluegtableigraphisobandlabelinglatticelifecyclemagrittrMatrixpkgconfigR6RColorBrewerrlangS7scalessurvivalvctrsviridisLitewithr

Cox Regression Under Quasi-Independent Double Truncation
Inverse probability weighted estimators | Plotting survival and hazard estimates | Assumption checking and model diagnostics | Testing quasi-independent truncation with covariates | Sensitivity analysis for positivity violations | Stratified Cox models | Time-varying covariates
Rendered fromcox-regression-quasiindep.Rmdusingknitr::rmarkdown

Last update: 2026-05-06
Started: 2026-05-06

Nonparametric Analysis of Doubly Truncated Data
Doubly truncated data | Nonparametric maximum likelihood estimation | Testing for event time differences across multiple groups | Assumptions required for the NPMLE | Testing for quasi-independent truncation | Testing for ignorable sampling bias
Rendered fromnonparametric-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-05-06
Started: 2026-05-06

Readme and manuals

Help Manual

Help pageTopics
AIDS incubation dataaids
Cox regression under quasi-independent double truncation.coef.coxph_indtrunc confint.coxph_indtrunc coxph_indtrunc print.coxph_indtrunc residuals.coxph_indtrunc vcov.coxph_indtrunc
Simulated data with non-proportional hazards and independent double truncationnonprop_sample
Compute the nonparametric MLE under double truncationis.npmle npmle print.npmle
Plot survival or hazard estimates from a Cox model fitplot_coxsurv
Plot estimates from an NPMLE fitplot.npmle
Sensitivity analysis for positivity violations in a Cox model with quasi-independent double truncation.plot.coxph_pos_indtrunc positivity_sens_indtrunc print.coxph_pos_indtrunc
Create a basic survival object for doubly truncated data.as.data.frame.Survdt is.na.Survdt is.Survdt length.Survdt plot.Survdt Survdt [.Survdt
Create a survival object for use with time-varying covariatesas.data.frame.Survdt2 is.na.Survdt2 is.Survdt2 length.Survdt2 plot.Survdt2 Survdt2 survdt2to1 [.Survdt2
Test for non-ignorable sampling biasplot.ignorability_indtrunc_test print.ignorability_indtrunc_test test_ignorability_indtrunc
Test for dependence between truncation times and the event time or covariatesplot.quasiindep_strat_test print.quasiindep_strat_test test_quasiindep_covariates
Test for dependence between truncation times and the event timeprint.quasiindep_ctau_test test_quasiindep_ctau
Test for survival differences across groupsplot.samplediff_indtrunc_test print.samplediff_indtrunc_test test_samplediff_indtrunc
Split rows in a data frame into multiple observations at specified time intervalstime_split