Comments on ‘Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian Joint Modeling Working Group’

Pharmacokinetic-pharmacodynamic modeling of tuberculosis time to positivity and colony-forming unit to assess the response to dose-ranging linezolid

According to the World Health Organization, the number of tuberculosis (TB) infections and the drug-resistant burden worldwide increased by 4.5% and 3.0%, respectively, between 2020 and 2021. Disease severity and complexity drive the interest for undertaking new clinical trials to provide efficient treatment to limit spread and drug resistance. TB Alliance conducted a phase 2 study in 106 patients to guide linezolid (LZD) dose selection using early bactericidal activity over 14 days of treatment. LZD is highly efficient for drug-resistant TB treatment, but treatment monitoring is required since serious adverse events can occur. The objective of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model to analyze the dose-response relationship between linezolid exposure and efficacy biomarkers. Using time to positivity (TTP) and colony-forming unit (CFU) count data, we developed a PKPD model in six dosing regimens, differing on LZD dosing intensity. A one-compartment model with five transit absorption compartments and non-linear auto-inhibition elimination described best LZD pharmacokinetic characteristics. TTP and CFU logarithmic scaled [log(CFU)] showed a bactericidal activity of LZD against Mycobacterium tuberculosis. TTP was defined by a model with two significant covariates: the presence of uni- and bilateral cavities decreased baseline TTP value by 24%, and an increase on every 500 mg/L/h of cumulative area under the curve increased the rate at which TTP and CFU change from baseline by 20% and 11%, respectively.

CLINICAL TRIALS

This study is registered with ClinicalTrials.gov as NCT02279875.

Bayesian joint modeling of longitudinal and spatial survival AIDS data

Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for analyzing cancer and AIDS data. As both repeated measurements (longitudinal) and time-to-event (survival) outcomes are observed in an individual, a joint modeling is more appropriate because it takes into account the dependence between the two types of responses, which are often analyzed separately. We propose a Bayesian hierarchical model for jointly modeling longitudinal and survival data considering functional time and spatial frailty effects, respectively. That is, the proposed model deals with non-linear longitudinal effects and spatial survival effects accounting for the unobserved heterogeneity among individuals living in the same region. This joint approach is applied to a cohort study of patients with HIV/AIDS in Brazil during the years 2002-2006. Our Bayesian joint model presents considerable improvements in the estimation of survival times of the Brazilian HIV/AIDS patients when compared with those obtained through a separate survival model and shows that the spatial risk of death is the same across the different Brazilian states. Copyright © 2016 John Wiley & Sons, Ltd.