| Name : R-sievePH
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| Version : 1.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.5.1
| Date : 2024-09-30 16:59:58
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| Group : Development/Libraries/Other
| Source RPM : R-sievePH-1.1-lp153.5.1.src.rpm
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| Size : 0.48 MB
| |
| Packager : (none)
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| Summary : Sieve Analysis Methods for Proportional Hazards Models
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Description :
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.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
