3 research outputs found
Joint Dispersion Model with a Flexible Link
The objective is to model longitudinal and survival data jointly taking into
account the dependence between the two responses in a real HIV/AIDS dataset
using a shared parameter approach inside a Bayesian framework. We propose a
linear mixed effects dispersion model to adjust the CD4 longitudinal biomarker
data with a between-individual heterogeneity in the mean and variance. In doing
so we are relaxing the usual assumption of a common variance for the
longitudinal residuals. A hazard regression model is considered in addition to
model the time since HIV/AIDS diagnostic until failure, being the coefficients,
accounting for the linking between the longitudinal and survival processes,
time-varying. This flexibility is specified using Penalized Splines and allows
the relationship to vary in time. Because heteroscedasticity may be related
with the survival, the standard deviation is considered as a covariate in the
hazard model, thus enabling to study the effect of the CD4 counts' stability on
the survival. The proposed framework outperforms the most used joint models,
highlighting the importance in correctly taking account the individual
heterogeneity for the measurement errors variance and the evolution of the
disease over time in bringing new insights to better understand this
biomarker-survival relation.Comment: 27 pages, 3 figures, 2 table
Comparing statistical methods in assessing the prognostic effect of biomarker variability on time-to-event clinical outcomes
BACKGROUND: In recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whether the variability of biomarkers is independently predictive of clinical outcomes. This question in most applications is addressed via a two-stage approach where summary statistics such as variance are calculated in the first stage and then used in models as covariates to predict clinical outcome in the second stage. The objective of this study is to compare the relative performance of various methods in estimating the effect of biomarker variability.
METHODS: A joint model and 4 different two-stage approaches (naïve, landmark analysis, time-dependent Cox model, and regression calibration) were illustrated using data from a large multi-center randomized phase III trial, the Ocular Hypertension Treatment Study (OHTS), regarding the association between the variability of intraocular pressure (IOP) and the development of primary open-angle glaucoma (POAG). The model performance was also evaluated in terms of bias using simulated data from the joint model of longitudinal IOP and time to POAG. The parameters for simulation were chosen after OHTS data, and the association between longitudinal and survival data was introduced via underlying, unobserved, and error-free parameters including subject-specific variance.
RESULTS: In the OHTS data, joint modeling and two-stage methods reached consistent conclusion that IOP variability showed no significant association with the risk of POAG. In the simulated data with no association between IOP variability and time-to-POAG, all the two-stage methods (except the naïve approach) provided a reliable estimation. When a moderate effect of IOP variability on POAG was imposed, all the two-stage methods underestimated the true association as compared with the joint modeling while the model-based two-stage method (regression calibration) resulted in the least bias.
CONCLUSION: Regression calibration and joint modelling are the preferred methods in assessing the effect of biomarker variability. Two-stage methods with sample-based measures should be used with caution unless there exists a relatively long series of longitudinal measurements and/or strong effect size (NCT00000125)