Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain kinds of nonlinear relationships between the outcome and a predictor. The R package chngpt provides both estimation and hypothesis testing functionalities for four common variants of threshold regression models. All allow for adjustment of additional covariates not subjected to thresholding. We demonstrate the consistency of the estimating procedures and the type 1 error rates of the testing procedures by Monte Carlo studies, and illustrate their practical uses using an example from the study of immune response biomarkers in the context of Mother-To-Child-Transmission of HIV-1 viruses. chngpt makes several unique contributions to the software for threshold regression models and will make these models more accessible to practitioners interested in modeling threshold effects.

Safe and effective vaccines are a critical component in the control of COVID-19. A group of industry, government, and academic researchers discuss pragmatic issues in the choice and interpretation ... Several vaccine candidates to protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) have entered or will soon enter large-sca...

One of the most successful HIV vaccines to date, the RV144 vaccine tested in Thailand, demonstrated correlates of protection including cross-clade V1V2 immunoglobulin G (IgG) breadth, Env-specific CD4⁺ T cell polyfunctionality, and antibody-dependent cellular cytotoxicity (ADCC) in vaccinees with low IgA binding. The HIV Vaccine Trials Network (HVTN) 097 trial evaluated this vaccine regimen in South Africa, where clade C HIV-1 predominates. We compared cellular and humoral responses at peak and durability immunogenicity time points in HVTN 097 and RV144 vaccinee samples, and evaluated vaccine-matched and cross-clade immune responses. At peak immunogenicity, HVTN 097 vaccinees exhibited significantly higher cellular and humoral immune responses than RV144 vaccinees. CD4⁺ T cell responses were more frequent in HVTN 097 irrespective of age and sex, and CD4⁺ T cell Env-specific functionality scores were higher in HVTN 097. Env-specific CD40L⁺ CD4⁺ T cells were more common in HVTN 097, with individuals having this pattern of expression demonstrating higher median antibody responses to HIV-1 Env. IgG and IgG3 binding antibody rates and response magnitude to gp120 vaccine– and V1V2 vaccine–matched antigens were higher or comparable in HVTN 097 than in RV144 ADCC, and ADCP functional antibody responses were elicited in HVTN 097. Env-specific IgG and CD4⁺ Env responses declined significantly over time in both trials. Overall, cross-clade immune responses associated with protection were better than expected in South Africa, suggesting wider applicability of this regimen.

Despite the heightened interest in developing biomarkers predicting treatment response that are used to optimize patient treatment decisions, there has been relatively little development of statistical methodology to evaluate these markers. There is currently no unified statistical framework for marker evaluation. This paper proposes a suite of descriptive and inferential methods designed to evaluate individual markers and to compare candidate markers. An R software package has been developed which implements these methods. Their utility is illustrated in the breast cancer treatment context, where candidate markers are evaluated for their ability to identify a subset of women who do not benefit from adjuvant chemotherapy and can therefore avoid its toxicity.