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Analyses of Repeatedly Measured Blood Pressure Data from A Cohort Study with Splines

Li, Jinhong (2020) Analyses of Repeatedly Measured Blood Pressure Data from A Cohort Study with Splines. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Cardiovascular disease (CVD) is a group of disease that involve the cardiovascular system. According to World Health Organization, it is the leading cause of death worldwide and hypertension is a major risk factor of CVD. Pregnancy is a special “window” for women and the physiological change could reflect enduring risk for CVD. In a recent study, researchers were interested in whether placental malperfusion could predict risk of CVD 8-10 years after delivery, and blood pressure (BP) is one of the endpoints of interest. In this thesis, we studied whether placental malperfusion is predictive for elevated BP. BP was repeatedly measured for three times during the office visit, thus the data had a longitudinal structure. One challenge is that BP fluctuates with regards to time during a day, and it is essential to adjust for the potential confounding effect of time in regression analyses. Splines provide a powerful tool to adjust for such relationship with abundant flexibility. In this thesis, natural cubic splines (NCS) and smoothing splines (SS) were considered and compared. As a consequence, application of the splines could identify significant predictive biomarkers, with flexible adjustment of the time effect. NCS is easier to use while SS is more flexibile.
Public health importance: Limited number of research have been done about the prognostic utility of placental malperfusion on risk of hypertension and CVD. Splines provide a powerful and flexible tool to characterize such a relationship.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Li, Jinhongjil250@pitt.edujil250
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairTang, Gonggot1@pitt.edu
Committee MemberCatov, Janetcatovjm@upmc.edu
Committee MemberLin, Yanyal14@pitt.edu
Date: 30 July 2020
Date Type: Publication
Defense Date: 17 April 2020
Approval Date: 30 July 2020
Submission Date: 15 May 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 43
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: natural cubic splines, smoothing splines, semiparametric mixed-effects models, blood pressure
Date Deposited: 30 Jul 2020 18:46
Last Modified: 30 Jul 2020 18:46
URI: http://d-scholarship.pitt.edu/id/eprint/39044

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