Literature DB >> 14652863

Comparing Cox and parametric models in clinical studies.

Alessandra Nardi1, Michael Schemper.   

Abstract

Parametric models are only occasionally used in the analysis of clinical studies of survival although they may offer advantages over Cox's model. In this paper, we report experiences that we have made fitting parametric models to data sets from different clinical trials mainly performed at the Vienna University Medical School. We emphasize the role of residuals for discriminating among candidate models and judging their goodness of fit. The effect of misspecification of the baseline distribution on parameter estimates and testing has been explored. The results from parametric analyses have always been contrasted with those from Cox's model. Copyright 2003 John Wiley & Sons, Ltd.

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Year:  2003        PMID: 14652863     DOI: 10.1002/sim.1592

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  23 in total

1.  Parametric proportional hazards model for mapping genomic imprinting of survival traits.

Authors:  Huijiang Gao; Yongxin Liu; Tingting Zhang; Runqing Yang; Daniel R Prows
Journal:  J Appl Genet       Date:  2012-11-07       Impact factor: 3.240

2.  Parametric modeling of localized melanoma prognosis and outcome.

Authors:  Shouluan Ding; Seng-Jaw Soong; Hui-Yi Lin; Renee Desmond; Charles M Balch
Journal:  J Biopharm Stat       Date:  2009-07       Impact factor: 1.051

3.  A Retrospective Cohort Study of Acute Kidney Injury Risk Associated with Antipsychotics.

Authors:  Yawen Jiang; Jeffrey S McCombs; Susie H Park
Journal:  CNS Drugs       Date:  2017-04       Impact factor: 5.749

4.  Multiplex Gene Profiling of Cell-Free DNA in Patients With Metastatic Melanoma for Monitoring Disease.

Authors:  Selena Y Lin; Sharon K Huang; Kelly T Huynh; Matthew P Salomon; Shu-Ching Chang; Diego M Marzese; Richard B Lanman; AmirAli Talasaz; Dave S B Hoon
Journal:  JCO Precis Oncol       Date:  2018-05-17

5.  Parametric survival analysis using R: Illustration with lung cancer data.

Authors:  Mukesh Kumar; Prashant Kr Sonker; Agni Saroj; Aanchal Jain; Atanu Bhattacharjee; Rakesh Kr Saroj
Journal:  Cancer Rep (Hoboken)       Date:  2019-07-24

6.  Accelerated failure time models provide a useful statistical framework for aging research.

Authors:  William R Swindell
Journal:  Exp Gerontol       Date:  2008-10-25       Impact factor: 4.032

7.  [Subgroup identification based on accelerated failure time model combined with adaptive elastic net].

Authors:  H Wei; P Kang; Y Liu; F Huang; Z Chen; S An
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2021-03-25

8.  Breast Cancer Survival Analysis: Applying the Generalized Gamma Distribution under Different Conditions of the Proportional Hazards and Accelerated Failure Time Assumptions.

Authors:  Alireza Abadi; Farzaneh Amanpour; Chris Bajdik; Parvin Yavari
Journal:  Int J Prev Med       Date:  2012-09

9.  Prognostic factors for the survival of patients with esophageal cancer in Northern Iran.

Authors:  Mahmood Reza Ghadimi; Mahboobeh Rasouli; Mahmood Mahmoodi; Kazem Mohammad
Journal:  J Res Med Sci       Date:  2011-10       Impact factor: 1.852

10.  Family history of the cancer on the survival of the patients with gastrointestinal cancer in northern Iran, using frailty models.

Authors:  Mahmoodreza Ghadimi; Mahmood Mahmoodi; Kazem Mohammad; Hojjat Zeraati; Mahboobeh Rasouli; Mahmood Sheikhfathollahi
Journal:  BMC Gastroenterol       Date:  2011-10-01       Impact factor: 3.067

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