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Regression modeling for competing risks based on pseudo-observations, with application to breast cancer study

Chen, Shangzhen (2019) Regression modeling for competing risks based on pseudo-observations, with application to breast cancer study. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

In medical research, a patient might experience a failure due to different causes, where each cause would be considered a competing risk. The standard method to analyze the type of data that is used most frequently in medical research is the cause-specific hazard regression analysis based on the Cox proportional hazards model or the cumulative incidence function-based method. The Cox proportional hazards model is used to model the cause-specific event rates and treat the other type of events as independent censoring. The new methods which have been proposed directly assess the effect of covariates on the cumulative incidence functions, which is the pseudo-observations approach proposed by Andersen and Klein in 2007. The scheme of pseudo-observations approach is to (1) choose some fixed time points that are equally spaced on the event scale, (2) calculate pseudo-observations for each individual at those fixed time points using the jackknife technique, and (3) fit a generalized linear model with GEE method based on the conditional cumulative incidence function using the pseudo-observations.
We applied the pseudo-observation approach to a breast cancer study. The goal of the study was to assess the effect of the covariates on the cumulative incidence function. The results showed that nodal status and tumor size are positively related to cumulative incidence of death following breast cancer recurrence and that age has a negative relationship with the cumulative incidence of death following breast cancer recurrence. In addition, nodal status and tumor size are not significantly associated with death due to causes other than breast cancer. Age is positively related to death not due to breast cancer.
Public health significance: Because the method explored and applied here is a readily accessible procedure for censored time-to-event data, providing straightforward interpretation of the effect of the predictors on the cumulative incidence function, dissemination of the method through a real world example would help the researchers, stakeholders,and patients in public health understand the usefulness of the method and grasp the interpretation of the analysis for competing risks data.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Chen, Shangzhenshc135@pitt.edushc1350000-0003-4978-1285
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorJeong, Jongjjeong@pitt.edujjeong
Committee MemberYouk, Adaayouk@pitt.eduayouk
Committee MemberLandsittel, Douglaslandsitteldp@upmc.edu
Date: 16 July 2019
Date Type: Publication
Defense Date: 5 June 2019
Approval Date: 16 July 2019
Submission Date: 3 June 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 46
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: pseudo-observations, generalized linear model with GEE
Date Deposited: 16 Jul 2019 17:20
Last Modified: 16 Jul 2019 17:20
URI: http://d-scholarship.pitt.edu/id/eprint/36864

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