03 Analysis of Longitudinal Data with Informative Right Censoring and Below Level of Detection Biomarke

Oregon Convention Center 

With advances in translational and biomedical sciences, and the availability of the sophisticated biotechnology, biomarkers continue to be important prognostic factors for risks of diseases that provide insights into mechanisms of treatment effectiveness and disease progression. Evaluating their effectiveness and predictive utility is often complicated by censoring due to failure of the medical instrument to measure a biological marker that falls below a certain threshold typically referred to as limit of detection (LOD). Values that fall below the detection limit are considered noise and unreliable, and are thus missing and left censored. If left censoring is ignored in the analysis then this will cause bias, inefficiency and inaccurate estimates. Model: We propose here a new approach for joint modeling of a covariate with values that are left censored due to falling below the level of detection of the measured biomarker. The left censoring process is jointly modelled with longitudinal measures that has informative right censoring and a discrete survival process. Interest is in assessing if the biomarker that has below detection level values along with other covariates of interest

Biomarkers

Discrete Survival

Left Censoring

Limit of Detection (LOD)

Longitudinal Model