Literature DB >> 33604061

SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.

Danilo Alvares1, Sebastien Haneuse2, Catherine Lee3, Kyu Ha Lee4.   

Abstract

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

Entities:  

Year:  2019        PMID: 33604061      PMCID: PMC7889044          DOI: 10.32614/rj-2019-038

Source DB:  PubMed          Journal:  R J        ISSN: 2073-4859            Impact factor:   3.984


  27 in total

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Journal:  Stat Med       Date:  2006-06-30       Impact factor: 2.373

2.  Causal inference for non-mortality outcomes in the presence of death.

Authors:  Brian L Egleston; Daniel O Scharfstein; Ellen E Freeman; Sheila K West
Journal:  Biostatistics       Date:  2006-09-15       Impact factor: 5.899

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Authors:  Luís Meira-Machado; Carmen Cadarso-Suárez; Jacobo de Uña-Alvarez
Journal:  Comput Methods Programs Biomed       Date:  2007-03-09       Impact factor: 5.428

4.  Estimating survival and association in a semicompeting risks model.

Authors:  Lajmi Lakhal; Louis-Paul Rivest; Belkacem Abdous
Journal:  Biometrics       Date:  2007-07-23       Impact factor: 2.571

5.  Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis.

Authors:  Kyu Ha Lee; Sebastien Haneuse; Deborah Schrag; Francesca Dominici
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2015-02-01       Impact factor: 1.864

6.  Bayesian gamma frailty models for survival data with semi-competing risks and treatment switching.

Authors:  Yuanye Zhang; Ming-Hui Chen; Joseph G Ibrahim; Donglin Zeng; Qingxia Chen; Zhiying Pan; Xiaodong Xue
Journal:  Lifetime Data Anal       Date:  2013-03-30       Impact factor: 1.588

7.  Time-to-event analysis when the event is defined on a finite time interval.

Authors:  Catherine Lee; Stephanie J Lee; Sebastien Haneuse
Journal:  Stat Methods Med Res       Date:  2019-08-22       Impact factor: 3.021

8.  Accelerated failure time models for semi-competing risks data in the presence of complex censoring.

Authors:  Kyu Ha Lee; Virginie Rondeau; Sebastien Haneuse
Journal:  Biometrics       Date:  2017-04-10       Impact factor: 2.571

9.  Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.

Authors:  Ina Jazić; Deborah Schrag; Daniel J Sargent; Sebastien Haneuse
Journal:  J Natl Cancer Inst       Date:  2016-07-05       Impact factor: 13.506

10.  Identification and estimation of survivor average causal effects.

Authors:  Eric J Tchetgen Tchetgen
Journal:  Stat Med       Date:  2014-05-29       Impact factor: 2.373

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Journal:  BMC Med Res Methodol       Date:  2022-10-12       Impact factor: 4.612

2.  Competing risks model for clustered data based on the subdistribution hazards with spatial random effects.

Authors:  Somayeh Momenyan; Farzane Ahmadi; Jalal Poorolajal
Journal:  J Appl Stat       Date:  2021-02-08       Impact factor: 1.416

  2 in total

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