Accessibility issues or difficulties with this website?
Call 919-962-2073 or email hchsadministration@unc.edu.

Modeling longitudinal change in biomarkers using data from a complex survey sampling design: An application to the Hispanic Community Health Study/Study of Latinos.

TitleModeling longitudinal change in biomarkers using data from a complex survey sampling design: An application to the Hispanic Community Health Study/Study of Latinos.
Publication TypePublication
Year2023
AuthorsButera NM, Zeng D, Heiss G, Cai J
JournalStat Med
Volume42
Issue5
Pagination632-655
Date Published2023 Feb 28
ISSN1097-0258
KeywordsBiomarkers, Cohort Studies, Hispanic or Latino, Humans, Public Health, Risk Factors
Abstract

In observational cohort studies, there is frequently interest in modeling longitudinal change in a biomarker (ie, physiological measure indicative of metabolic dysregulation or disease; eg, blood pressure) in the absence of treatment (ie, medication), and its association with modifiable risk factors expected to affect health (eg, body mass index). However, individuals may start treatment during the study period, and consequently biomarker values observed while on treatment may be different than those that would have been observed in the absence of treatment. If treated individuals are excluded from analysis, then effect estimates may be biased if treated individuals differ systematically from untreated individuals. We addressed this concern in the setting of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an observational cohort study that employed a complex survey sampling design to enable inference to a finite target population. We considered biomarker values measured while on treatment to be missing data, and applied missing data methodology (inverse probability weighting (IPW) and doubly robust estimation) to this problem. The proposed methods leverage information collected between study visits on when individuals started treatment, by adapting IPW and doubly robust approaches to model the treatment mechanism using survival analysis methods. This methodology also incorporates sampling weights and uses a bootstrap approach to estimate standard errors accounting for the complex survey sampling design. We investigated variance estimation for these methods, conducted simulation studies to assess statistical performance in finite samples, and applied the methodology to model temporal change in blood pressure in HCHS/SOL.

DOI10.1002/sim.9635
Alternate JournalStat Med
PubMed ID36631123
PubMed Central IDPMC10936944
Grant ListHHSN268201300005C / HL / NHLBI NIH HHS / United States
HHSN268201300004C / HL / NHLBI NIH HHS / United States
75N92019D00010 / HL / NHLBI NIH HHS / United States
N01HC65236 / HL / NHLBI NIH HHS / United States
N01HC65233 / HL / NHLBI NIH HHS / United States
N01HC65237 / HL / NHLBI NIH HHS / United States
HHSN268201300003C / HG / NHGRI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
N01HC65235 / HL / NHLBI NIH HHS / United States
U01 DK098246 / DK / NIDDK NIH HHS / United States
HHSN268201300001C / HB / NHLBI NIH HHS / United States
N01HC65234 / HL / NHLBI NIH HHS / United States
MS#: 
0775
Manuscript Lead/Corresponding Author Affiliation: 
Coordinating Center - Collaborative Studies Coordinating Center - UNC at Chapel Hill
ECI: 
Yes
Manuscript Affiliation: 
Coordinating Center - Collaborative Studies Coordinating Center - UNC at Chapel Hill
Manuscript Status: 
Published