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Modeling exposure-time-response association in the presence of competing risks

Li, Xingyuan (2019) Modeling exposure-time-response association in the presence of competing risks. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

In longitudinal pharmacoepidemiology studies, exposures may be chronic over a period of time and the intensity, duration, and timing of the exposures may vary among subjects. Further challenges may arise when the data involve competing risks, where subjects may fail from one of multiple events and failure from one precludes the risk of experiencing others.

A model that predicts the risk of a health outcome from the longitudinal pattern of exposures can help the researchers and health care professionals identify high-risk individuals more efficiently. However, methodological challenges arise in at least three aspects: 1) how to model time-varying exposure? 2) is there a cumulative and latency effect such that exposures could contribute to the risk incrementally over time? and 3) how to handle competing risks?

The dissertation focuses on how to overcome these challenges in the development of methods for directly modeling the probability of the main event of interest, i.e., cumulative incidence function (CIF). In the first section, we propose a subdistribution hazards regression model. The model incorporated weighted cumulative effects of the exposure so the intensity, duration, and the timing of the exposure can be taken into consideration simultaneously. We incorporated penalized cubic $B$-splines into the partial likelihood equation to estimate the weights.

In the second section, we propose a generalized transformation regression model. We extended the model in the first section and allowed the subdistribution hazard ratio to be a bivariate function of both the time lag and the level of time-varying exposures. We also introduced various link functions to model the CIF. We used penalized tensor product splines to flexibly estimate the bidimensional surface for the cumulative effects of exposure, and incorporated an additional ridge penalty to constrain the model behavior.

We showed adequate performance of the proposed models through simulations. In the real data analysis, we investigated the association between patterns of prescription opioid use and the risk of overdose for Medicare and Medicaid beneficiaries, treating mortality as a competing risk.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Li, Xingyuanxingyuanli96@gmail.comxil1430000-0001-7234-728X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChang, Chung-Chou H.changj@pitt.edu
Committee MemberAnderson, Stewart Jsja@pitt.edu
Committee MemberDonohue, Julie Mjdonohue@pitt.edu
Committee MemberCheng, Yuyucheng@pitt.edu
Committee MemberKrafty, Robert Trkrafty@pitt.edu
Date: 27 June 2019
Date Type: Publication
Defense Date: 18 January 2019
Approval Date: 27 June 2019
Submission Date: 1 February 2019
Access Restriction: 3 year -- Restrict access to University of Pittsburgh for a period of 3 years.
Number of Pages: 71
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: time-varying exposures; cumulative effect; lagged association; competing risks; cumulative incidence function; subdistribution hazards; generalized transformation model.
Date Deposited: 27 Jun 2019 14:16
Last Modified: 27 Jun 2019 14:16
URI: http://d-scholarship.pitt.edu/id/eprint/35952

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