Package: sievePH 1.1

Michal Juraska

sievePH: Sieve Analysis Methods for Proportional Hazards Models

Implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 <doi:10.1007/s10985-015-9353-9>. Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified mark‐specific proportional hazards models with missing marks. Scandinavian Journal of Statistics}, 39(1):34-52 <doi:10.1111/j.1467-9469.2011.00746.x>, and in Gilbert PB and Sun Y (2015), Inferences on relative failure rates in stratified mark-specific proportional hazards models with missing marks, with application to human immunodeficiency virus vaccine efficacy trials. Journal of the Royal Statistical Society Series C: Applied Statistics, 64(1):49-73 <doi:10.1111/rssc.12067>. Both semiparametric and nonparametric approaches consider two scenarios: (1) the mark is fully observed in all subjects who experience the event of interest, and (2) the mark is subject to missingness-at-random in subjects who experience the event of interest. For models with missing marks, estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases (for the semiparametric framework), and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the augmentation term for subjects with missing marks (for both the semiparametric and nonparametric framework). The augmented IPW estimators are doubly robust and recommended for use with incomplete mark data. The semiparametric methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results.

Authors:Michal Juraska [aut, cre], Li Li [ctb], Stephanie Wu [ctb]

sievePH_1.1.tar.gz
sievePH_1.1.tar.gz(r-4.5-noble)sievePH_1.1.tar.gz(r-4.4-noble)
sievePH_1.1.tgz(r-4.4-emscripten)sievePH_1.1.tgz(r-4.3-emscripten)
sievePH.pdf |sievePH.html
sievePH/json (API)
NEWS

# Install 'sievePH' in R:
install.packages('sievePH', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mjuraska/sieveph/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

1.70 score 8 scripts 221 downloads 8 exports 73 dependencies

Last updated 7 months agofrom:5bcddf1070. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-linux-x86_64OKNov 14 2024

Exports:ggplot_sievekernel_sievePHkernel_sievePHaipwsievePHsievePHaipwsievePHipwtestDensRatioGOFtestIndepTimeMark

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11cubatureDerivdoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnpnumDerivpbkrtestpillarpkgconfigplyrpolynompurrrquadprogquantregR6RColorBrewerRcppRcppArmadilloRcppEigenrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Plotting Univariate Mark-Specific Proportional Hazards Model Fits Using 'ggplot'ggplot_sieve
Nonparametric Kernel-Smoothed Stratified Mark-Specific Proportional Hazards Model with a Univariate Continuous Mark, Fully Observed in All Failures.kernel_sievePH
Nonparametric Kernel-Smoothed Stratified Mark-Specific Proportional Hazards Model with a Univariate Continuous Mark, Missing-at-Random in Some Failureskernel_sievePHaipw
Plotting Mark-Specific Proportional Hazards Model Fitsplot.summary.sievePH
Semiparametric Estimation of Coefficients in a Mark-Specific Proportional Hazards Model with a Multivariate Continuous Mark, Fully Observed in All FailuressievePH
Semiparametric Augmented Inverse Probability Weighted Complete-Case Estimation of Coefficients in a Mark-Specific Proportional Hazards Model with a Multivariate Continuous Mark, Missing-at-Random in Some FailuressievePHaipw
Semiparametric Inverse Probability Weighted Complete-Case Estimation of Coefficients in a Mark-Specific Proportional Hazards Model with a Multivariate Continuous Mark, Missing-at-Random in Some FailuressievePHipw
Summarizing Nonparametric Kernel-Smoothed Stratified Mark-Specific Proportional Hazards Model Fitsprint.summary.kernel_sievePH summary.kernel_sievePH
Summarizing Mark-Specific Proportional Hazards Model Fitsprint.summary.sievePH summary.sievePH
Goodness-of-Fit Test of the Validity of a Univariate or Multivariate Mark Density Ratio ModeltestDensRatioGOF
Kolmogorov-Smirnov-Type Test of Conditional Independence between the Time-to-Event and a Multivariate Mark Given TreatmenttestIndepTimeMark