Title | Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates. |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Chen, Qingxia, Ryan C. May, Joseph G. Ibrahim, Haitao Chu, and Stephen R. Cole |
Journal | Stat Med |
Volume | 33 |
Issue | 26 |
Pagination | 4560-76 |
Date Published | 2014 Nov 20 |
ISSN | 1097-0258 |
Keywords | Bayes Theorem, CD4 Lymphocyte Count, Cohort Studies, HIV, HIV Infections, Humans, Limit of Detection, Longitudinal Studies, Proportional Hazards Models, Survival Analysis, Viral Load |
Abstract | We propose a joint model for longitudinal and survival data with time-varying covariates subject to detection limits and intermittent missingness at random. The model is motivated by data from the Multicenter AIDS Cohort Study (MACS), in which HIV+ subjects have viral load and CD4 cell count measured at repeated visits along with survival data. We model the longitudinal component using a normal linear mixed model, modeling the trajectory of CD4 cell count by regressing on viral load, and other covariates. The viral load data are subject to both left censoring because of detection limits (17%) and intermittent missingness (27%). The survival component of the joint model is a Cox model with time-dependent covariates for death because of AIDS. The longitudinal and survival models are linked using the trajectory function of the linear mixed model. A Bayesian analysis is conducted on the MACS data using the proposed joint model. The proposed method is shown to improve the precision of estimates when compared with alternative methods. |
DOI | 10.1002/sim.6242 |
Alternate Journal | Stat Med |
Original Publication | Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates. |
PubMed ID | 24947785 |
PubMed Central ID | PMC4189992 |
Grant List | R21AI103012 / AI / NIAID NIH HHS / United States P30 CA077598 / CA / NCI NIH HHS / United States R01AI100654 / AI / NIAID NIH HHS / United States 1P01CA142538 / CA / NCI NIH HHS / United States R24 AI067039 / AI / NIAID NIH HHS / United States R01 GM070335 / GM / NIGMS NIH HHS / United States R21 AI103012 / AI / NIAID NIH HHS / United States 2P30CA077598 / CA / NCI NIH HHS / United States U01 AI103390 / AI / NIAID NIH HHS / United States R01 AI100654 / AI / NIAID NIH HHS / United States R24AI067039 / AI / NIAID NIH HHS / United States P30 AI050410 / AI / NIAID NIH HHS / United States R21 HL097334 / HL / NHLBI NIH HHS / United States R21HL097334 / HL / NHLBI NIH HHS / United States 1UL1RR024975 / RR / NCRR NIH HHS / United States UL1 TR000445 / TR / NCATS NIH HHS / United States P30AI50410 / AI / NIAID NIH HHS / United States GM 70335 / GM / NIGMS NIH HHS / United States T32 CA106209 / CA / NCI NIH HHS / United States P01CA142538 / CA / NCI NIH HHS / United States UL1 RR024975 / RR / NCRR NIH HHS / United States U01AI103390 / AI / NIAID NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States |
Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates.
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