Abstract Number:
3885
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Miran A. Jaffa (1)
Institutions:
(1) N/A, N/A
First Author:
Presenting Author:
Abstract Text:
Biological marker when measured may fall 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.
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 and the longitudinal trajectories of a biomarker of interest compounded have any effect on the informative dropout mechanism. We assume that the contribution of the undetected (left censored) observation to the joint likelihood function is through a mass below the level of detection defined by a cumulative distribution function of standard normal distribution.
Keywords:
Biomarkers|Discrete Survival|Left Censoring|Limit of Detection (LOD)|Longitudinal Model|
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