Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Pharmacology & Pharmacy, Statistics & Probability, Mathematics, persistence criteria, prediction, serial correlation, threshold, DISEASE PROGRESSION, LYMPHOCYTE COUNT, INFECTION, MORTALITY, INTERVALS, Aortic Aneurysm, Abdominal, Biomarkers, CD4 Lymphocyte Count, Cohort Studies, Disease Progression, HIV Infections, Humans, Markov Chains, Models, Statistical, Probability, Time Factors, 0104 Statistics, 1115 Pharmacology and Pharmaceutical Sciences, 3214 Pharmacology and pharmaceutical sciences, 4905 Statistics

Abstract:

Biomarkers play a key role in the monitoring of disease progression. The time taken for an individual to reach a biomarker exceeding or lower than a meaningful threshold is often of interest. Due to the inherent variability of biomarkers, persistence criteria are sometimes included in the definitions of progression, such that only two consecutive measurements above or below the relevant threshold signal that "true" progression has occurred. In previous work, a novel approach was developed, which allowed estimation of the time to threshold using the parameters from a linear mixed model where the residual variance was assumed to be pure measurement error. In this paper, we extend this methodology so that serial correlation can be accommodated. Assuming that the Markov property holds and applying the chain rule of probabilities, we found that the probability of progression at each timepoint can be expressed simply as the product of conditional probabilities. The methodology is applied to a cohort of HIV positive individuals, where the time to reach a CD4 count threshold is estimated. The second application we present is based on a study on abdominal aortic aneurysms, where the time taken for an individual to reach a diameter exceeding 55 mm is studied. We observed that erroneously ignoring the residual correlation when it is strong may result in substantial overestimation of the time to threshold. The estimated probability of the biomarker reaching a threshold of interest, expected time to threshold, and confidence intervals are presented for selected patients in both applications.