Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

INFERENCE ON SURVIVAL DATA UNDER NONPROPORTIONAL HAZARDS

Xu, Qing (2007) INFERENCE ON SURVIVAL DATA UNDER NONPROPORTIONAL HAZARDS. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img]
Preview
PDF
Primary Text

Download (1MB) | Preview

Abstract

The objective of this research is to develop optimal (efficient) test methods for analysis of survival data under random censorship with nonproportional hazards. For the first part we revisit the weighted log-rank test where the weight function was derived by assuming the inverse Gaussian distribution for an omitted exponentiated covariate that induces a nonproportionality under the proportional hazards model. We perform a simulation study to compare the new procedure with ones using other popular weight functions including members of the Harrington-Fleming's G-rho family. The nonproportional hazards data are generated by changing the hazard ratios over time under the proportional hazards model. The results indicate that the inverse Gaussian-based test tends to have higher power than some of the members that belong to the G-rho family in detecting a difference between two survival distributions when populations become homogeneous as time progresses. The second part of the research includes development of a parametric method in detecting the validity of the proportional odds model assumption between two groups of survival data. The research is based on the premise that the test procedure developed would take advantage of knowledge of the distributional information about the data, which will improve the sensitivity of a nonparametric test method. We evaluate type I error and power probabilities of the new parametric test by using the simulated survival data following the log-logistic distribution. The error probabilities are compared with ones in the literature. The results indicate that the extended test performs with a higher sensitivity than the existing nonparametric method.The results from the proposed study provide statistical test methods that are more sensitive than existing ones under certain situations which can be used in public health relevance applications such as clinical trials.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Xu, Qingqix2@pitt.edu, pinkcatqw45@yahoo.comQIX2
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairJeong, Jong-Hyeonjeong@nsabp.pitt.eduJJEONG
Committee MemberWahed, Abduswahed@pitt.eduWAHED
Committee MemberKingsley, Lawrencekingsley@pitt.eduKINGSLEY
Committee MemberAnderson, Stewartsja@pitt.eduSJA
Date: 21 June 2007
Date Type: Completion
Defense Date: 23 April 2007
Approval Date: 21 June 2007
Submission Date: 11 April 2007
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: weighted logrank; survival analysis; frailty; nonproportional hazards
Other ID: http://etd.library.pitt.edu/ETD/available/etd-04112007-115536/, etd-04112007-115536
Date Deposited: 10 Nov 2011 19:35
Last Modified: 15 Nov 2016 13:39
URI: http://d-scholarship.pitt.edu/id/eprint/7001

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item