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.
In the coronavirus efficacy (COVE) phase 3 clinical trial, vaccine recipients were assessed for neutralizing and binding antibodies as correlates of risk for COVID-19 disease and as correlates of ...protection. These immune markers were measured at the time of second vaccination and 4 weeks later, with values reported in standardized World Health Organization international units. All markers were inversely associated with COVID-19 risk and directly associated with vaccine efficacy. Vaccine recipients with postvaccination 50% neutralization titers 10, 100, and 1000 had estimated vaccine efficacies of 78% (95% confidence interval, 54 to 89%), 91% (87 to 94%), and 96% (94 to 98%), respectively. These results help define immune marker correlates of protection and may guide approval decisions for messenger RNA (mRNA) COVID-19 vaccines and other COVID-19 vaccines.
While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a ...small number of cases-a common situation in biomedical studies, which often have rare outcomes and covariates whose measurement is resource-intensive.
Using an immunologic marker dataset from a phase III HIV vaccine efficacy trial, we seek to optimize random forest prediction performance using combinations of variable screening, class balancing, weighting, and hyperparameter tuning.
Our experiments show that while class balancing helps improve random forest prediction performance when variable screening is not applied, class balancing has a negative impact on performance in the presence of variable screening. The impact of the weighting similarly depends on whether variable screening is applied. Hyperparameter tuning is ineffective in situations with small sample sizes. We further show that random forests under-perform generalized linear models for some subsets of markers, and prediction performance on this dataset can be improved by stacking random forests and generalized linear models trained on different subsets of predictors, and that the extent of improvement depends critically on the dissimilarities between candidate learner predictions.
In small datasets from two-phase sampling design, variable screening and inverse sampling probability weighting are important for achieving good prediction performance of random forests. In addition, stacking random forests and simple linear models can offer improvements over random forests.
Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or ...immune reaction, remains a challenge.
We first assess the performance of 18 deep learning-based cell segmentation models, either pre-trained or trained by us using two public image sets, on a set of immunofluorescence images stained with immune cell surface markers in skin tissue obtained during human herpes simplex virus (HSV) infection. We then further train eight of these models using up to 10,000+ training instances from the current image set. Finally, we seek to improve performance by tuning parameters of the most successful method from the previous step.
The best model before fine-tuning achieves a mean Average Precision (mAP) of 0.516. Prediction performance improves substantially after training. The best model is the cyto model from Cellpose. After training, it achieves an mAP of 0.694; with further parameter tuning, the mAP reaches 0.711.
Selecting the best model among the existing approaches and further training the model with images of interest produce the most gain in prediction performance. The performance of the resulting model compares favorably to human performance. The imperfection of the final model performance can be attributed to the moderate signal-to-noise ratio in the imageset.
Several studies have shown that neutralizing antibody levels correlate with immune protection from COVID-19 and have estimated the relationship between neutralizing antibodies and protection. ...However, results of these studies vary in terms of estimates of the level of neutralizing antibodies required for protection. By normalizing antibody titers, we found that study results converge on a consistent relationship between antibody levels and protection from COVID-19. This finding can be useful for planning future vaccine use, determining population immunity, and reducing the global effects of the COVID-19 pandemic.
In the RV144 trial, the estimated efficacy of a vaccine regimen against human immunodeficiency virus type 1 (HIV-1) was 31.2%. We performed a case-control analysis to identify antibody and cellular ...immune correlates of infection risk.
In pilot studies conducted with RV144 blood samples, 17 antibody or cellular assays met prespecified criteria, of which 6 were chosen for primary analysis to determine the roles of T-cell, IgG antibody, and IgA antibody responses in the modulation of infection risk. Assays were performed on samples from 41 vaccinees who became infected and 205 uninfected vaccinees, obtained 2 weeks after final immunization, to evaluate whether immune-response variables predicted HIV-1 infection through 42 months of follow-up.
Of six primary variables, two correlated significantly with infection risk: the binding of IgG antibodies to variable regions 1 and 2 (V1V2) of HIV-1 envelope proteins (Env) correlated inversely with the rate of HIV-1 infection (estimated odds ratio, 0.57 per 1-SD increase; P=0.02; q=0.08), and the binding of plasma IgA antibodies to Env correlated directly with the rate of infection (estimated odds ratio, 1.54 per 1-SD increase; P=0.03; q=0.08). Neither low levels of V1V2 antibodies nor high levels of Env-specific IgA antibodies were associated with higher rates of infection than were found in the placebo group. Secondary analyses suggested that Env-specific IgA antibodies may mitigate the effects of potentially protective antibodies.
This immune-correlates study generated the hypotheses that V1V2 antibodies may have contributed to protection against HIV-1 infection, whereas high levels of Env-specific IgA antibodies may have mitigated the effects of protective antibodies. Vaccines that are designed to induce higher levels of V1V2 antibodies and lower levels of Env-specific IgA antibodies than are induced by the RV144 vaccine may have improved efficacy against HIV-1 infection.
Analysis of correlates of risk of infection in the RV144 HIV-1 vaccine efficacy trial demonstrated that plasma IgG against the HIV-1 envelope (Env) variable region 1 and 2 inversely correlated with ...risk, whereas HIV-1 Env-specific plasma IgA responses directly correlated with risk. In the secondary analysis, antibody-dependent cellular cytotoxicity (ADCC) was another inverse correlate of risk, but only in the presence of low plasma IgA Env-specific antibodies. Thus, we investigated the hypothesis that IgA could attenuate the protective effect of IgG responses through competition for the same Env binding sites. We report that Env-specific plasma IgA/IgG ratios are higher in infected than in uninfected vaccine recipients in RV144. Moreover, Env-specific IgA antibodies from RV144 vaccinees blocked the binding of ADCC-mediating mAb to HIV-1 Env glycoprotein 120 (gp120). An Env-specific monomeric IgA mAb isolated from an RV144 vaccinee also inhibited the ability of natural killer cells to kill HIV-1–infected CD4 ⁺ T cells coated with RV144-induced IgG antibodies. We show that monomeric Env-specific IgA, as part of postvaccination polyclonal antibody response, may modulate vaccine-induced immunity by diminishing ADCC effector function.
In the RV144 HIV-1 vaccine efficacy trial, IgG antibody (Ab) binding levels to variable regions 1 and 2 (V1V2) of the HIV-1 envelope glycoprotein gp120 were an inverse correlate of risk of HIV-1 ...infection. To determine if V1V2-specific Abs cross-react with V1V2 from different HIV-1 subtypes, if the nature of the V1V2 antigen used to asses cross-reactivity influenced infection risk, and to identify immune assays for upcoming HIV-1 vaccine efficacy trials, new V1V2-scaffold antigens were designed and tested. Protein scaffold antigens carrying the V1V2 regions from HIV-1 subtypes A, B, C, D or CRF01_AE were assayed in pilot studies, and six were selected to assess cross-reactive Abs in the plasma from the original RV144 case-control cohort (41 infected vaccinees, 205 frequency-matched uninfected vaccinees, and 40 placebo recipients) using ELISA and a binding Ab multiplex assay. IgG levels to these antigens were assessed as correlates of risk in vaccine recipients using weighted logistic regression models. Levels of Abs reactive with subtype A, B, C and CRF01_AE V1V2-scaffold antigens were all significant inverse correlates of risk (p-values of 0.0008-0.05; estimated odds ratios of 0.53-0.68 per 1 standard deviation increase). Thus, levels of vaccine-induced IgG Abs recognizing V1V2 regions from multiple HIV-1 subtypes, and presented on different scaffolds, constitute inverse correlates of risk for HIV-1 infection in the RV144 vaccine trial. The V1V2 antigens provide a link between RV144 and upcoming HIV-1 vaccine trials, and identify reagents and methods for evaluating V1V2 Abs as possible correlates of protection against HIV-1 infection.
ClinicalTrials.gov NCT00223080.
In the PREVENT-19 phase 3 trial of the NVX-CoV2373 vaccine (NCT04611802), anti-spike binding IgG concentration (spike IgG), anti-RBD binding IgG concentration (RBD IgG), and pseudovirus 50% ...neutralizing antibody titer (nAb ID50) measured two weeks post-dose two are assessed as correlates of risk and as correlates of protection against COVID-19. Analyses are conducted in the U.S. cohort of baseline SARS-CoV-2 negative per-protocol participants using a case-cohort design that measures the markers from all 12 vaccine recipient breakthrough COVID-19 cases starting 7 days post antibody measurement and from 639 vaccine recipient non-cases. All markers are inversely associated with COVID-19 risk and directly associated with vaccine efficacy. In vaccine recipients with nAb ID50 titers of 50, 100, and 7230 international units (IU50)/ml, vaccine efficacy estimates are 75.7% (49.8%, 93.2%), 81.7% (66.3%, 93.2%), and 96.8% (88.3%, 99.3%). The results support potential cross-vaccine platform applications of these markers for guiding decisions about vaccine approval and use.
Over the past several years, only approximately 50% of HIV-exposed infants received an early infant diagnosis test within the first two months of life. While high attrition and mortality account for ...some of the shortcomings in identifying HIV-infected infants early and putting them on life-saving treatment, fragmented and challenging laboratory systems are an added barrier. We sought to determine the accuracy of using HIV viral load assays for infant diagnosis of HIV. We enrolled 866 Ugandan infants between March-April 2018 for this study after initial laboratory diagnosis. The median age was seven months, while 33% of infants were less than three months of age. Study testing was done using either the Roche or Abbott molecular technologies at the Central Public Health Laboratory. Dried blood spot samples were prepared according to manufacturer-recommended protocols for both the qualitative and quantitative assays. Viral load test samples for the Roche assay were processed using two different buffers: phosphate-buffered saline (PBS: free virus elution viral load protocol FVE) and Sample Pre-Extraction Reagent (SPEX: qualitative buffer). Dried blood spot samples were processed for both assays on the Abbott using the manufacturer's standard infant diagnosis protocol. All infants received a qualitative test for clinical management and additional paired quantitative tests. 858 infants were included in the analysis, of which 50% were female. Over 75% of mothers received antiretroviral therapy, while approximately 65% of infants received infant prophylaxis. The Roche SPEX and Abbott technologies had high sensitivity (>95%) and specificity (>98%). The Roche FVE had lower sensitivity (85%) and viral load values. To simplify and streamline laboratory practices, HIV viral load may be used to diagnose HIV infection in infants, particularly using the Roche SPEX and Abbott technologies.
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