Literature DB >> 18836831

Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data.

Hans C van Houwelingen1, Hein Putter.   

Abstract

This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the first author and the established multi-state modeling as described in a recent Tutorial in Biostatistics in Statistics in Medicine by the second author and colleagues. As expected the two approaches give similar results. The landmark methodology does not need complex modeling and leads to easy prediction rules. On the other hand, it does not give the insight in the biological processes as obtained for the multi-state model.

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Year:  2008        PMID: 18836831      PMCID: PMC2798037          DOI: 10.1007/s10985-008-9099-8

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  8 in total

1.  Multi-state models for bone marrow transplantation studies.

Authors:  John P Klein; Youyi Shu
Journal:  Stat Methods Med Res       Date:  2002-04       Impact factor: 3.021

2.  Estimating average regression effect under non-proportional hazards.

Authors:  R Xu; J O'Quigley
Journal:  Biostatistics       Date:  2000-12       Impact factor: 5.899

3.  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

4.  Tutorial in biostatistics: competing risks and multi-state models.

Authors:  H Putter; M Fiocco; R B Geskus
Journal:  Stat Med       Date:  2007-05-20       Impact factor: 2.373

5.  Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models.

Authors:  Marta Fiocco; Hein Putter; Hans C van Houwelingen
Journal:  Stat Med       Date:  2008-09-20       Impact factor: 2.373

6.  Partly conditional survival models for longitudinal data.

Authors:  Yingye Zheng; Patrick J Heagerty
Journal:  Biometrics       Date:  2005-06       Impact factor: 2.571

7.  Estimation and prediction in a multi-state model for breast cancer.

Authors:  Hein Putter; Jos van der Hage; Geertruida H de Bock; Rachid Elgalta; Cornelis J H van de Velde
Journal:  Biom J       Date:  2006-06       Impact factor: 2.207

8.  Plotting summary predictions in multistate survival models: probabilities of relapse and death in remission for bone marrow transplantation patients.

Authors:  J P Klein; N Keiding; E A Copelan
Journal:  Stat Med       Date:  1993-12-30       Impact factor: 2.373

  8 in total
  35 in total

1.  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

2.  A randomized, double-blind, phase III trial of personalized peptide vaccination for recurrent glioblastoma.

Authors:  Yoshitaka Narita; Yoshiki Arakawa; Fumiyuki Yamasaki; Ryo Nishikawa; Tomokazu Aoki; Masayuki Kanamori; Motoo Nagane; Toshihiro Kumabe; Yuichi Hirose; Tomotsugu Ichikawa; Hiroyuki Kobayashi; Takamitsu Fujimaki; Hisaharu Goto; Hideo Takeshima; Tetsuya Ueba; Hiroshi Abe; Takashi Tamiya; Yukihiko Sonoda; Atsushi Natsume; Tatsuyuki Kakuma; Yasuo Sugita; Nobukazu Komatsu; Akira Yamada; Tetsuro Sasada; Satoko Matsueda; Shigeki Shichijo; Kyogo Itoh; Mizuhiko Terasaki
Journal:  Neuro Oncol       Date:  2019-02-19       Impact factor: 12.300

3.  Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial.

Authors:  Layla Parast; Lu Tian; Tianxi Cai
Journal:  J Am Stat Assoc       Date:  2014-01-01       Impact factor: 5.033

4.  Joint multiple imputation for longitudinal outcomes and clinical events that truncate longitudinal follow-up.

Authors:  Bo Hu; Liang Li; Tom Greene
Journal:  Stat Med       Date:  2015-07-15       Impact factor: 2.373

5.  Landmark Linear Transformation Model for Dynamic Prediction with Application to a Longitudinal Cohort Study of Chronic Disease.

Authors:  Yayuan Zhu; Liang Li; Xuelin Huang
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2018-12-23       Impact factor: 1.864

6.  Time-dependent prognostic score matching for recurrent event analysis to evaluate a treatment assigned during follow-up.

Authors:  Abigail R Smith; Douglas E Schaubel
Journal:  Biometrics       Date:  2015-08-21       Impact factor: 2.571

7.  Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease.

Authors:  Liang Li; Sheng Luo; Bo Hu; Tom Greene
Journal:  Stat Biosci       Date:  2016-11-07

Review 8.  The use of ambulatory assessment in smoking cessation.

Authors:  Christine Vinci; Aaron Haslam; Cho Y Lam; Santosh Kumar; David W Wetter
Journal:  Addict Behav       Date:  2018-02-03       Impact factor: 3.913

9.  Landmark risk prediction of residual life for breast cancer survival.

Authors:  Layla Parast; Tianxi Cai
Journal:  Stat Med       Date:  2013-03-14       Impact factor: 2.373

10.  A two-stage approach for dynamic prediction of time-to-event distributions.

Authors:  Xuelin Huang; Fangrong Yan; Jing Ning; Ziding Feng; Sangbum Choi; Jorge Cortes
Journal:  Stat Med       Date:  2016-01-07       Impact factor: 2.373

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