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cff-version: 1.2.0
message: 'To cite package "sievePH" in publications use:'
type: software
license: GPL-2.0-only
title: 'sievePH: Sieve Analysis Methods for Proportional Hazards Models'
version: '1.1'
doi: 10.32614/CRAN.package.sievePH
abstract: '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
  <https://doi.org/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
  <https://doi.org/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 <https://doi.org/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 <https://doi.org/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:
- family-names: Juraska
  given-names: Michal
  email: mjuraska@fredhutch.org
repository: https://mjuraska.r-universe.dev
repository-code: https://github.com/mjuraska/sievePH
commit: b1493e2354937f25121923b3366b81538ffd9e9c
url: https://github.com/mjuraska/sievePH
date-released: '2024-05-15'
contact:
- family-names: Juraska
  given-names: Michal
  email: mjuraska@fredhutch.org