Literature DB >> 1391990

Flexible covariate effects in the proportional hazards model.

T Hastie1, L Sleeper, R Tibshirani.   

Abstract

The proportional hazards model is frequently used in analyzing the results of clinical trials, when it is often the case that the outcomes are right-censored. This model allows one to measure treatment effects and simultaneously identify and adjust for prognostic factors that might influence the outcome. In this paper, we outline a class of semiparametric models that allows one to model prognostic factors nonlinearly, and have the data suggest the form of their effect. The methods are illustrated in an analysis of data from a breast cancer clinical trial.

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Year:  1992        PMID： 1391990     DOI： 10.1007/bf01840837

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN： 0167-6806            Impact factor:   4.872


  3 in total

1.  A plain man's guide to the proportional hazards model.

Authors:  R Tibshirani
Journal:  Clin Invest Med       Date:  1982       Impact factor: 0.825

2.  Six-year results of the Eastern Cooperative Oncology Group trial of observation versus CMFP versus CMFPT in postmenopausal patients with node-positive breast cancer.

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Journal:  J Clin Oncol       Date:  1989-07       Impact factor: 44.544

3.  Importance of prognostic factors in cancer clinical trials.

Authors:  R Simon
Journal:  Cancer Treat Rep       Date:  1984-01
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Review 2.  Statistical aspects of prognostic factor studies in oncology.

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4.  On the Use of Fractional Polynomial Models to Assess Preventive Aspect of Variables: An Example in Prevention of Mortality Following HIV Infection.

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