Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Statistics in Medici...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Statistics in Medicine
Article . 2008 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
versions View all 2 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Sensitivity analysis of progression‐free survival with dependent withdrawal

Authors: Robert Gray; Ping K. Ruan;

Sensitivity analysis of progression‐free survival with dependent withdrawal

Abstract

AbstractWe develop a sensitivity analysis method for comparing treatment‐specific distributions where the endpoint is progression‐free survival (PFS). The censoring process may be informative due to selective patient withdrawal, which occurs whenever disease evaluation has been discontinued without progression being documented. The sensitivity analysis explores the effects of the dependence between patient withdrawal and progression time using a conditional probability model which incorporates a set of sensitivity parameters. We propose an EM algorithm for estimation of PFS under the model for dependence and construct log‐rank‐type score statistics from the estimated distributions. Bootstrap procedures are used to estimate the variance of the score statistic. We also extend the methodology to incorporate additional survival information, which may be available on the cases who were withdrawn. An Eastern Cooperative Oncology Group (ECOG) advanced lung cancer clinical trial (E1594) is used to illustrate the methodology. Copyright © 2007 John Wiley & Sons, Ltd.

Keywords

Lung Neoplasms, Models, Statistical, Patient Dropouts, Endpoint Determination, Medical Oncology, Sensitivity and Specificity, Disease-Free Survival, Research Design, Disease Progression, Humans, Algorithms, Randomized Controlled Trials as Topic

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Top 10%
Average
Related to Research communities
Cancer Research
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!