Literature DB >> 18199521

SAS and R functions to compute pseudo-values for censored data regression.

John P Klein1, Mette Gerster, Per Kragh Andersen, Sergey Tarima, Maja Pohar Perme.   

Abstract

Recently, in a series of papers, a method based on pseudo-values has been proposed for direct regression modeling of the survival function, the restricted mean and cumulative incidence function with right censored data. The models, once the pseudo-values have been computed, can be fit using standard generalized estimating equation software. Here we present SAS macros and R functions to compute these pseudo-values. We illustrate the use of these routines and show how to obtain regression estimates for a study of bone marrow transplant patients.

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Year:  2008        PMID: 18199521      PMCID: PMC2533132          DOI: 10.1016/j.cmpb.2007.11.017

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  6 in total

1.  Estimating equations for association structures.

Authors:  Jun Yan; Jason Fine
Journal:  Stat Med       Date:  2004-03-30       Impact factor: 2.373

2.  Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function.

Authors:  John P Klein; Per Kragh Andersen
Journal:  Biometrics       Date:  2005-03       Impact factor: 2.571

3.  Regression analysis of restricted mean survival time based on pseudo-observations.

Authors:  Per Kragh Andersen; Mette Gerster Hansen; John P Klein
Journal:  Lifetime Data Anal       Date:  2004-12       Impact factor: 1.588

4.  Modelling competing risks in cancer studies.

Authors:  John P Klein
Journal:  Stat Med       Date:  2006-03-30       Impact factor: 2.373

5.  Analyzing survival curves at a fixed point in time.

Authors:  John P Klein; Brent Logan; Mette Harhoff; Per Kragh Andersen
Journal:  Stat Med       Date:  2007-10-30       Impact factor: 2.373

6.  Treatment for acute myelocytic leukemia with allogeneic bone marrow transplantation following preparation with BuCy2.

Authors:  E A Copelan; J C Biggs; J M Thompson; P Crilley; J Szer; J P Klein; N Kapoor; B R Avalos; I Cunningham; K Atkinson
Journal:  Blood       Date:  1991-08-01       Impact factor: 22.113

  6 in total
  38 in total

1.  SAS macros for estimation of direct adjusted cumulative incidence curves under proportional subdistribution hazards models.

Authors:  Xu Zhang; Mei-Jie Zhang
Journal:  Comput Methods Programs Biomed       Date:  2010-08-17       Impact factor: 5.428

2.  Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.

Authors:  Lili Zhao; Susan Murray; Laura H Mariani; Wenjun Ju
Journal:  Stat Med       Date:  2020-07-27       Impact factor: 2.373

3.  The use and interpretation of competing risks regression models.

Authors:  James J Dignam; Qiang Zhang; Masha Kocherginsky
Journal:  Clin Cancer Res       Date:  2012-01-26       Impact factor: 12.531

Review 4.  Applying competing risks regression models: an overview.

Authors:  Bernhard Haller; Georg Schmidt; Kurt Ulm
Journal:  Lifetime Data Anal       Date:  2012-09-26       Impact factor: 1.588

5.  Modeling cumulative incidence function for competing risks data.

Authors:  Mei-Jie Zhang; Xu Zhang; Thomas H Scheike
Journal:  Expert Rev Clin Pharmacol       Date:  2008-05-01       Impact factor: 5.045

6.  Deep Neural Networks for Survival Analysis Using Pseudo Values.

Authors:  Lili Zhao; Dai Feng
Journal:  IEEE J Biomed Health Inform       Date:  2020-11-04       Impact factor: 5.772

7.  Competing risk of death: an important consideration in studies of older adults.

Authors:  Sarah D Berry; Long Ngo; Elizabeth J Samelson; Douglas P Kiel
Journal:  J Am Geriatr Soc       Date:  2010-03-22       Impact factor: 5.562

8.  Estimate risk difference and number needed to treat in survival analysis.

Authors:  Zhongheng Zhang; Federico Ambrogi; Alex F Bokov; Hongqiu Gu; Edwin de Beurs; Khaled Eskaf
Journal:  Ann Transl Med       Date:  2018-04

9.  Competing mortality and fracture risk assessment.

Authors:  W D Leslie; L M Lix; X Wu
Journal:  Osteoporos Int       Date:  2012-06-27       Impact factor: 4.507

10.  Events per variable for risk differences and relative risks using pseudo-observations.

Authors:  Stefan Nygaard Hansen; Per Kragh Andersen; Erik Thorlund Parner
Journal:  Lifetime Data Anal       Date:  2014-01-14       Impact factor: 1.588

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