Literature DB >> 34565525

Adjusted Survival Curves Improve Understanding of Multivariable Cox Model Results.

Carly S Lundgreen1, Dirk R Larson1, Elizabeth J Atkinson1, Katrina L Devick2, David G Lewallen3, Daniel J Berry3, Hilal Maradit Kremers4, Cynthia S Crowson5.   

Abstract

Kaplan-Meier survival curves are the most common methods for unadjusted group comparison of outcomes in orthopedic research. However, they may be misleading due to an imbalance of confounders between patient groups. The Cox model is frequently used to adjust for confounders, but graphical display of adjusted survival curves is not commonly utilized. We describe the circumstances when adjusted survival curves are useful in orthopedic research, describe and use 2 different methods to obtain adjusted curves, and illustrate how they can improve understanding of the multivariable Cox model results. We further provide practical strategies for identifying the need for and performing adjusted survival curves. Please visit the followinghttps://youtu.be/ys0hy2CiMCAfor a video that explains the highlights of the paper in practical terms.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cox model; confounding; survival analysis; survival curves; total joint arthroplasty

Mesh:

Year:  2021        PMID: 34565525      PMCID: PMC8476943          DOI: 10.1016/j.arth.2021.06.002

Source DB:  PubMed          Journal:  J Arthroplasty        ISSN: 0883-5403            Impact factor:   4.435


  9 in total

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Authors:  Stephen R Cole; Miguel A Hernán
Journal:  Comput Methods Programs Biomed       Date:  2004-07       Impact factor: 5.428

Review 2.  Review of case-mix corrected survival curves.

Authors:  Todd A MacKenzie; Jeremiah R Brown; Donald S Likosky; YingXing Wu; Gary L Grunkemeier
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Authors:  M E Charlson; P Pompei; K L Ales; C R MacKenzie
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4.  Long-Term Mortality Trends After Revision Total Knee Arthroplasty.

Authors:  Jie J Yao; Mario Hevesi; Megan M O'Byrne; Daniel J Berry; David G Lewallen; Hilal Maradit Kremers
Journal:  J Arthroplasty       Date:  2018-12-01       Impact factor: 4.757

5.  Comparison of 2 methods for calculating adjusted survival curves from proportional hazards models.

Authors:  W A Ghali; H Quan; R Brant; G van Melle; C M Norris; P D Faris; P D Galbraith; M L Knudtson
Journal:  JAMA       Date:  2001-09-26       Impact factor: 56.272

6.  Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems.

Authors:  Stuart J Pocock; Susan E Assmann; Laura E Enos; Linda E Kasten
Journal:  Stat Med       Date:  2002-10-15       Impact factor: 2.373

7.  Direct adjusted survival and cumulative incidence curves for observational studies.

Authors:  Zhen-Huan Hu; Robert Peter Gale; Mei-Jie Zhang
Journal:  Bone Marrow Transplant       Date:  2019-05-17       Impact factor: 5.483

8.  The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies.

Authors:  Brennan C Kahan; Vipul Jairath; Caroline J Doré; Tim P Morris
Journal:  Trials       Date:  2014-04-23       Impact factor: 2.279

9.  An evaluation of inverse probability weighting using the propensity score for baseline covariate adjustment in smaller population randomised controlled trials with a continuous outcome.

Authors:  Hanaya Raad; Victoria Cornelius; Susan Chan; Elizabeth Williamson; Suzie Cro
Journal:  BMC Med Res Methodol       Date:  2020-03-23       Impact factor: 4.615

  9 in total
  1 in total

1.  Prognostic value of triglyceride glucose (TyG) index in patients with acute decompensated heart failure.

Authors:  Rong Huang; Ziyan Wang; Jianzhou Chen; Xue Bao; Nanjiao Xu; Simin Guo; Rong Gu; Weimin Wang; Zhonghai Wei; Lian Wang
Journal:  Cardiovasc Diabetol       Date:  2022-05-31       Impact factor: 8.949

  1 in total

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