A latent class approach for joint modeling of a time-to-event outcome and multiple longitudinal biomarkers subject to limits of detection

Menghan Li, Ching Wen Lee, Lan Kong

Research output: Contribution to journalArticle

Abstract

Multiple biomarkers on different biological pathways are often measured over time to investigate the complex mechanism of disease development and progression. Identification of informative subpopulation patterns of longitudinal biomarkers and clinical endpoint may assist in risk stratification and provide insights into new therapeutic targets. Motivated by a multicenter study to assess the inflammatory markers of sepsis in patients with community-acquired pneumonia, we propose a joint latent class analysis of multiple biomarkers and a time-to-event outcome while accounting for censored biomarker measurements due to detection limits. The interrelationship between biomarker trajectories and clinical endpoint is fully captured by a latent class structure, which reveals the subpopulation profiles of biomarkers and clinical outcome. The estimation of joint latent class models becomes more complicated when biomarkers are subject to detection limits. Based on a Metropolis–Hastings method, we develop a Monte Carlo Expectation–Maximization (MCEM) algorithm to estimate model parameters. We demonstrate the satisfactory performance of our MCEM algorithm using simulation studies, and apply our method to the motivating study to examine the heterogeneous patterns of cytokine responses to pneumonia and associated mortality risks.

Original languageEnglish (US)
JournalStatistical Methods in Medical Research
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Latent Class
Joint Modeling
Biomarkers
Limit of Detection
Joints
Detection Limit
Monte Carlo Algorithm
Expectation-maximization Algorithm
Pneumonia
Latent Class Analysis
Metropolis-Hastings
Latent Class Model
Cytokines
Joint Model
Stratification
Progression
Mortality
Multicenter Studies
Disease Progression
Pathway

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

@article{a0e2693f2ed74c0b8e8edc94c0aa94f6,
title = "A latent class approach for joint modeling of a time-to-event outcome and multiple longitudinal biomarkers subject to limits of detection",
abstract = "Multiple biomarkers on different biological pathways are often measured over time to investigate the complex mechanism of disease development and progression. Identification of informative subpopulation patterns of longitudinal biomarkers and clinical endpoint may assist in risk stratification and provide insights into new therapeutic targets. Motivated by a multicenter study to assess the inflammatory markers of sepsis in patients with community-acquired pneumonia, we propose a joint latent class analysis of multiple biomarkers and a time-to-event outcome while accounting for censored biomarker measurements due to detection limits. The interrelationship between biomarker trajectories and clinical endpoint is fully captured by a latent class structure, which reveals the subpopulation profiles of biomarkers and clinical outcome. The estimation of joint latent class models becomes more complicated when biomarkers are subject to detection limits. Based on a Metropolis–Hastings method, we develop a Monte Carlo Expectation–Maximization (MCEM) algorithm to estimate model parameters. We demonstrate the satisfactory performance of our MCEM algorithm using simulation studies, and apply our method to the motivating study to examine the heterogeneous patterns of cytokine responses to pneumonia and associated mortality risks.",
author = "Menghan Li and Lee, {Ching Wen} and Lan Kong",
year = "2019",
month = "1",
day = "1",
doi = "10.1177/0962280219871679",
language = "English (US)",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",

}

TY - JOUR

T1 - A latent class approach for joint modeling of a time-to-event outcome and multiple longitudinal biomarkers subject to limits of detection

AU - Li, Menghan

AU - Lee, Ching Wen

AU - Kong, Lan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Multiple biomarkers on different biological pathways are often measured over time to investigate the complex mechanism of disease development and progression. Identification of informative subpopulation patterns of longitudinal biomarkers and clinical endpoint may assist in risk stratification and provide insights into new therapeutic targets. Motivated by a multicenter study to assess the inflammatory markers of sepsis in patients with community-acquired pneumonia, we propose a joint latent class analysis of multiple biomarkers and a time-to-event outcome while accounting for censored biomarker measurements due to detection limits. The interrelationship between biomarker trajectories and clinical endpoint is fully captured by a latent class structure, which reveals the subpopulation profiles of biomarkers and clinical outcome. The estimation of joint latent class models becomes more complicated when biomarkers are subject to detection limits. Based on a Metropolis–Hastings method, we develop a Monte Carlo Expectation–Maximization (MCEM) algorithm to estimate model parameters. We demonstrate the satisfactory performance of our MCEM algorithm using simulation studies, and apply our method to the motivating study to examine the heterogeneous patterns of cytokine responses to pneumonia and associated mortality risks.

AB - Multiple biomarkers on different biological pathways are often measured over time to investigate the complex mechanism of disease development and progression. Identification of informative subpopulation patterns of longitudinal biomarkers and clinical endpoint may assist in risk stratification and provide insights into new therapeutic targets. Motivated by a multicenter study to assess the inflammatory markers of sepsis in patients with community-acquired pneumonia, we propose a joint latent class analysis of multiple biomarkers and a time-to-event outcome while accounting for censored biomarker measurements due to detection limits. The interrelationship between biomarker trajectories and clinical endpoint is fully captured by a latent class structure, which reveals the subpopulation profiles of biomarkers and clinical outcome. The estimation of joint latent class models becomes more complicated when biomarkers are subject to detection limits. Based on a Metropolis–Hastings method, we develop a Monte Carlo Expectation–Maximization (MCEM) algorithm to estimate model parameters. We demonstrate the satisfactory performance of our MCEM algorithm using simulation studies, and apply our method to the motivating study to examine the heterogeneous patterns of cytokine responses to pneumonia and associated mortality risks.

UR - http://www.scopus.com/inward/record.url?scp=85072078429&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85072078429&partnerID=8YFLogxK

U2 - 10.1177/0962280219871679

DO - 10.1177/0962280219871679

M3 - Article

C2 - 31469042

AN - SCOPUS:85072078429

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

ER -