Ensemble clustering of longitudinal bivariate HIV biomarker profiles to group patients by patterns of disease progression.

This paper describes an ensemble cluster analysis of bivariate profiles of HIV biomarkers, viral load and CD4 cell counts, which jointly measure disease progression. Data are from a prevalent cohort of HIV positive participants in a clinical trial of vitamin supplementation in Botswana. These individuals were HIV positive upon enrollment, but with unknown times of infection. To categorize groups of participants based on their patterns of progression of HIV infection using both biomarkers, we combine univariate shape-based cluster results for multiple biomarkers through the use of ensemble clustering methods. We first describe univariate clustering for each of the individual biomarker profiles, and make use of shape-respecting distances for clustering the longitudinal profile data. In our data, profiles are subject to either missing or irregular measurements as well as unobserved initiation times of the process of interest. Shape-respecting distances that can handle such data issues, preserve time-ordering, and identify similar profile shapes are useful in identifying patterns of disease progression from longitudinal biomarker data. However, their performance with regard to clustering differs by severity of the data issues mentioned above. We provide an empirical investigation of shape-respecting distances (Fréchet and dynamic time warping (DTW)) on benchmark shape data, and use DTW in cluster analysis of biomarker profile observations. These reveal a primary group of 'typical progressors,' as well as a smaller group that shows relatively rapid progression. We then refine the analysis using ensemble clustering for both markers to obtain a single classification. The information from joint evaluation of the two biomarkers combined with ensemble clustering reveals subgroups of patients not identifiable through univariate analyses; noteworthy subgroups are those that appear to represent recently and chronically infected subsets. Supplementary Information: The online version contains supplementary material available at 10.1007/s41060-022-00323-2.

The vitamin D for COVID-19 (VIVID) trial: A pragmatic cluster-randomized design.

OBJECTIVES: To determine the effect of vitamin D supplementation on disease progression and post-exposure prophylaxis for COVID-19 infection. We hypothesize that high-dose vitamin D3 supplementation will reduce risk of hospitalization/death among those with recently diagnosed COVID-19 infection and will reduce risk of COVID-19 infection among their close household contacts. METHODS: We report the rationale and design of a planned pragmatic, cluster randomized, double-blinded trial (N = 2700 in total nationwide), with 1500 newly diagnosed individuals with COVID-19 infection, together with up to one close household contact each (~1200 contacts), randomized to either vitamin D3 (loading dose, then 3200 IU/day) or placebo in a 1:1 ratio and a household cluster design. The study duration is 4 weeks. The primary outcome for newly diagnosed individuals is the occurrence of hospitalization and/or mortality. Key secondary outcomes include symptom severity scores among cases and changes in the infection (seroconversion) status for their close household contacts. Changes in vitamin D 25(OH)D levels will be assessed and their relation to study outcomes will be explored. CONCLUSIONS: The proposed pragmatic trial will allow parallel testing of vitamin D3 supplementation for early treatment and post-exposure prophylaxis of COVID-19. The household cluster design provides a cost-efficient approach to testing an intervention for reducing rates of hospitalization and/or mortality in newly diagnosed cases and preventing infection among their close household contacts.

Bayesian estimation of mixture models with prespecified elements to compare drug resistance in treatment-naïve and experienced tuberculosis cases.

We propose a Bayesian approach for estimating branching tree mixture models to compare drug-resistance pathways (i.e. patterns of sequential acquisition of resistance to individual antibiotics) that are observed among Mycobacterium tuberculosis isolates collected from treatment-naïve and treatment-experienced patients. Resistant pathogens collected from treatment-naïve patients are strains for which fitness costs of resistance were not sufficient to prevent transmission, whereas those collected from treatment-experienced patients reflect both transmitted and acquired resistance, the latter of which may or may not be associated with lower transmissibility. The comparison of the resistance pathways constructed from these two groups of drug-resistant strains provides insight into which pathways preferentially lead to the development of multiple drug resistant strains that are transmissible. We apply the proposed statistical methods to data from worldwide surveillance of drug-resistant tuberculosis collected by the World Health Organization over 13 years.

Implications of HIV PrEP trials results.

Abstract Six randomized clinical trials have been implemented to examine the efficacy of tenofovir disoproxil fumarate (TDF) and/or TDF/emtricitabine (TDF/FTC) as preexposure prophylaxis for HIV-1 infection (PrEP). Although largely complementary, the six trials have many similar features. As the earliest results become available, an urgent question may arise regarding whether changes should be made in the conduct of the other trials. To consider this in advance, a Consultation on the Implications of HIV Pre-Exposure Prophylaxis (PrEP) Trials Results sponsored by the Division of AIDS (DAIDS) of the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), and the Bill and Melinda Gates Foundation (BMGF) was held on January 29, 2010, at the Natcher Conference Center, NIH, Bethesda, MD. Participants included basic scientists, clinical researchers (including investigators performing the current PrEP trials), and representatives from the U.S. Food and Drug Administration (FDA) and the agencies sponsoring the trials: the U.S. Centers for Disease Control and Prevention (CDC), the U.S. Agency for International Development (USAID), the BMGF, and the U.S. NIH. We report here a summary of the presentations and highlights of salient discussion topics from this workshop.