Associations Between HIV and Severe Mpox in an Atlanta Cohort.

BACKGROUND: In the Southeastern United States, the 2022 mpox outbreak disproportionately impacted people who are black and people with HIV (PWH). METHODS: We analyzed a cohort of 395 individuals diagnosed with mpox across 3 health care systems in Atlanta, Georgia between 1 June 2022 and 7 October 2022. We present demographic and clinical characteristics and use multivariable logistic regression analyses to evaluate the association between HIV status and severe mpox (per the US Centers for Disease Control and Prevention definition) and, among PWH, the associations between CD4+ T-cell count and HIV load with severe mpox. RESULTS: Of 395 people diagnosed with mpox, 384 (97.2%) were cisgender men, 335 (84.8%) identified as black, and 324 (82.0%) were PWH. Of 257 PWH with a known HIV load, 90 (35.0%) had > 200 copies/mL. Severe mpox occurred in 77 (19.5%) individuals and there was 1 (0.3%) death. Tecovirimat was prescribed to 112 (28.4%) people, including 56 (72.7%) people with severe mpox. In the multivariable analysis of the total population, PWH had 2.52 times higher odds of severe mpox (95% confidence interval [CI], 1.01-6.27) compared with people without HIV. In the multivariable analysis of PWH, individuals with HIV load > 200 copies/mL had 2.10 (95% CI, 1.00-4.39) times higher odds of severe mpox than PWH who were virologically suppressed. Lower CD4+ T-cell count showed a significant univariate association with severe mpox but was not found to be significantly associated with severe mpox in multivariable analysis. CONCLUSIONS: PWH with nonsuppressed HIV loads had more mpox complications, hospitalizations, and protracted disease courses than people without HIV or PWH with suppressed viral loads. PWH with nonsuppressed HIV loads who are diagnosed with mpox warrant particularly aggressive monitoring and treatment.

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations.

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g., in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g., means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone.

Sensitivity and Uncertainty Analysis for Two-stream Capture-Recapture Methods in Disease Surveillance.

Capture-recapture methods are widely applied in estimating the number ( ) of prevalent or cumulatively incident cases in disease surveillance. Here, we focus the bulk of our attention on the common case in which there are 2 data streams. We propose a sensitivity and uncertainty analysis framework grounded in multinomial distribution-based maximum likelihood, hinging on a key dependence parameter that is typically nonidentifiable but is epidemiologically interpretable. Focusing on the epidemiologically meaningful parameter unlocks appealing data visualizations for sensitivity analysis and provides an intuitively accessible framework for uncertainty analysis designed to leverage the practicing epidemiologist's understanding of the implementation of the surveillance streams as the basis for assumptions driving estimation of . By illustrating the proposed sensitivity analysis using publicly available HIV surveillance data, we emphasize both the need to admit the lack of information in the observed data and the appeal of incorporating expert opinion about the key dependence parameter. The proposed uncertainty analysis is a simulation-based approach designed to more realistically acknowledge variability in the estimated associated with uncertainty in an expert's opinion about the nonidentifiable parameter, together with the statistical uncertainty. We demonstrate how such an approach can also facilitate an appealing general interval estimation procedure to accompany capture-recapture methods. Simulation studies illustrate the reliable performance of the proposed approach for quantifying uncertainties in estimating in various contexts. Finally, we demonstrate how the recommended paradigm has the potential to be directly extended for application to data from >2 surveillance streams.

Tailoring capture-recapture methods to estimate registry-based case counts based on error-prone diagnostic signals.

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.

A censored quantile regression approach for relative survival analysis: Relative survival quantile regression.

We propose a censored quantile regression model for the analysis of relative survival data. We create a hybrid data set consisting of the study observations and counterpart randomly sampled pseudopopulation observations imputed from population life tables that adjust for expected mortality. We then fit a censored quantile regression model to the hybrid data incorporating demographic variables (e.g., age, biologic sex, calendar time) corresponding to the population life tables of demographically-similar individuals, a population versus study covariate, and its interactions with the variables of interest. These latter variables can be interpreted as relative survival parameters that depict the differences in failure quantiles between the study participants and their population counterparts.

Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.

Novel application of one-step pooled molecular testing and maximum likelihood approaches to estimate the prevalence of malaria parasitaemia among rapid diagnostic test negative samples in western Kenya.

BACKGROUND: Detection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular tools. While pooled testing using a two-step strategy provides a cost-saving alternative to the gold standard of individual sample testing, statistical adjustments are needed to improve accuracy of prevalence estimates for a single step pooled testing strategy. METHODS: A random sample of 4670 malaria RDT negative dried blood spot samples were selected from a mass testing and treatment trial in Asembo, Gem, and Karemo, western Kenya. Samples were tested for malaria individually and in pools of five, 934 pools, by one-step quantitative polymerase chain reaction (qPCR). Maximum likelihood approaches were used to estimate subpatent parasitaemia (RDT-negative, qPCR-positive) prevalence by pooling, assuming poolwise sensitivity and specificity was either 100% (strategy A) or imperfect (strategy B). To improve and illustrate the practicality of this estimation approach, a validation study was constructed from pools allocated at random into main (734 pools) and validation (200 pools) subsets. Prevalence was estimated using strategies A and B and an inverse-variance weighted estimator and estimates were weighted to account for differential sampling rates by area. RESULTS: The prevalence of subpatent parasitaemia was 14.5% (95% CI 13.6-15.3%) by individual qPCR, 9.5% (95% CI (8.5-10.5%) by strategy A, and 13.9% (95% CI 12.6-15.2%) by strategy B. In the validation study, the prevalence by individual qPCR was 13.5% (95% CI 12.4-14.7%) in the main subset, 8.9% (95% CI 7.9-9.9%) by strategy A, 11.4% (95% CI 9.9-12.9%) by strategy B, and 12.8% (95% CI 11.2-14.3%) using inverse-variance weighted estimator from poolwise validation. Pooling, including a 20% validation subset, reduced costs by 52% compared to individual testing. CONCLUSIONS: Compared to individual testing, a one-step pooled testing strategy with an internal validation subset can provide accurate prevalence estimates of PCR-positivity among RDT-negatives at a lower cost.

Differences in inpatient and outpatient hepatitis C virus prevalence and linkage to care rates in a safety net hospital hepatitis C screening program.

BACKGROUND AND AIM: Routine screening for hepatitis C virus (HCV) infection is crucial in identifying the 50% of infected persons unaware of their infection. We added an inpatient screening initiative to our successful outpatient HCV screening program in an urban, safety-net hospital. METHODS: From March 2017 to December 2019, HCV screening was performed in inpatient and outpatient settings at Grady Health System. We compared care cascade outcomes, including anti-HCV testing, RNA testing, and linkage to care (LTC) between these settings. RESULTS: A total of 29 751 patients were tested for anti-HCV: 8883 inpatients and 20 868 outpatients. The anti-HCV population was predominantly Black (76.2%) and male (67.9%). The total anti-HCV prevalence was 8.9%, with 14% of inpatients and 6.7% of outpatients testing positive. RNA testing was performed on 86%. The prevalence of active HCV infection was 59.3% in those that were anti-HCV positive; inpatient prevalence was 66%, and outpatient was 53.8%. Of those with active infection, 67.5% were linked to care (57.3% of inpatients and 77.8% of outpatients). CONCLUSION: We found significant differences in prevalence of anti-HCV and LTC rates between inpatients and outpatients during an HCV screening program. Higher anti-HCV prevalence among inpatients may be due a higher prevalence of non-birth year HCV risk factors. LTC rates were lower in the inpatient setting despite a robust linkage strategy. The striking prevalence of HCV in both settings warrants continued screening, expansion to additional settings, and novel strategies to improve inpatient linkage rates, especially in the setting of new universal HCV screening guidelines.

Both Handwashing and an Alcohol-Based Hand Sanitizer Intervention Reduce Soil and Microbial Contamination on Farmworker Hands during Harvest, but Produce Type Matters.

Hand hygiene interventions are critical for reducing farmworker hand contamination and preventing the spread of produce-associated illness. Hand hygiene effectiveness may be produce-commodity specific, which could influence implementation strategies. This study's goal was to determine if produce commodity influences the ability of handwashing with soap and water or two-step alcohol-based hand sanitizer (ABHS) interventions to reduce soil and bacteria on farmworker hands. Farmworkers (n = 326) harvested produce (cantaloupe, jalapeño, and tomato) for 30 to 90 minutes before engaging in handwashing, two-step ABHS (jalapeño and cantaloupe), or no hand hygiene. Hands were rinsed to measure amounts of soil (absorbance at 600 nm) and indicator bacteria (coliforms, Enterococcus sp., generic Escherichia coli, and Bacteroidales universal [AllBac] and human-specific [BFD] 16S rRNA gene markers). Without hand hygiene, bacterial concentrations (0.88 to 5.1 log10 CFU/hand) on hands significantly differed by the produce commodity harvested. Moderate significant correlations (ρ = -0.41 to 0.56) between soil load and bacterial concentrations were observed. There were significant produce-commodity-specific differences in the ability of handwashing and two-step ABHS interventions to reduce soil (P < 0.0001), coliforms (P = 0.002), and Enterococcus sp. (P = 0.003), but not the Bacteroidales markers AllBac (P = 0.4) or BFD (P = 0.3). Contamination on hands of farmworkers who harvested cantaloupe was more difficult to remove. Overall, we found that a two-step ABHS intervention was similar to handwashing with soap and water at reducing bacteria on farmworker hands. In summary, produce commodity type should be considered when developing hand hygiene interventions on farms.IMPORTANCE This study demonstrated that the type of produce commodity handled influences the ability of handwashing with soap and water or a two-step alcohol-based hand sanitizer (ABHS) intervention to reduce soil and bacterial hand contamination. Handwashing with soap and water, as recommended by the FDA's Produce Safety Rule, when tested in three agricultural environments, does not always reduce bacterial loads. Consistent with past results, we found that the two-step ABHS method performed similarly to handwashing with soap and water but also does not always reduce bacterial loads in these contexts. Given the ease of use of the two-step ABHS method, which may increase compliance, the two-step ABHS method should be further evaluated and possibly considered for implementation in the agricultural environment. Taken together, these results provide important information on hand hygiene effectiveness in three agricultural contexts.

Formulas and Web Application for Designing a Biospecimen Pooling Study to Compare Group Means.

BACKGROUND: When research focuses on biomarker assessment in settings where per-assay costs are high relative to per-subject costs, a biospecimen pooling study design can be extremely cost-effective. However, designing a study to maximize cost savings is complicated by the fact that pooled measurements are typically subject to processing error, inducing additional variability caused by combining biospecimens, and may also be affected by assay-related measurement error. METHODS: We provide formulas and an interactive web application (hereafter called app) for designing a pooling study to compare group means. Power and sample size formulas are justified by Central Limit Theorem arguments that make no distributional assumptions on the biomarker. Errors can be assumed mean-0 additive or mean-1 multiplicative, the latter being well-suited for skewed biomarkers. RESULTS: User inputs for the app include usual power parameters as well as per-assay and per-subject costs and information about the errors: which are present, whether they are additive or multiplicative, and their variances. The app generates plots revealing the optimal pool size, required number of assays, cost savings, and sensitivity to the hard-to-predict processing error variance. CONCLUSIONS: These tools should aid in the design and deployment of pooling studies powered to detect group mean differences while minimizing total study costs.

Drought severity and all-cause mortality rates among adults in the United States: 1968-2014.

BACKGROUND: Little is known about the effect of drought on all-cause mortality, especially in higher income countries such as the United States. As the frequency and severity of droughts are likely to increase, understanding the connections between drought and mortality becomes increasingly important. METHODS: Our exposure variable was an annual cumulative drought severity score based on the 1-month, county-level Standardized Precipitation Evapotranspiration Index. The outcome variables of demographic subgroup-specific all-cause mortality count data per year were obtained from the National Vital Statistics System. Any counts below 10 deaths were censored in that demographic group per county. We modeled county-stratum-year mortality using interval-censored negative binomial regression with county-level random intercepts, for each combined age-race-sex stratum either with or without further stratification by climate regions. Fixed effects meta-regression was used to test the associations between age, race, sex, and region with the drought-mortality regression coefficients. Predictive margins were then calculated from the meta-regression model to estimate larger subgroup (e.g., 'race' or 'sex') associations of drought with mortality. RESULTS: Most of the results were null for associations between drought severity and mortality, across joint strata of race, age, sex and region, but incidence rate ratios (IRRs) for 17 subgroups were significant after accounting for the multiple testing; ten were < 1 indicating a possible protective effect of drought on mortality for that particular subpopulation. The meta-regression indicated heterogeneity in the association of drought with mortality according to race, climate region, and age, but not by sex. Marginal means of the estimated log-incidence rate ratios differed significantly from zero for age groups 25-34, 35-44, 45-54 and 55-64; for the white race group; and for the South, West and Southwest regions, in the analysis that included wet county-years. The margin of the meta-regression model suggested a slightly negative, but not statistically significant, association of drought with same-year mortality in the overall population. CONCLUSIONS: There were significant, heterogeneous-direction associations in subpopulation-stratified models, after controlling for multiple comparisons, suggesting that the impacts of drought on mortality may not be monolithic across the United States. Meta-regression identified systematic differences in the associations of drought severity with all-cause mortality according to climate region, race, and age. These findings suggest there may be important contextual differences in the effects of drought severity on mortality, motivating further work focused on local mechanisms. We speculate that some of the estimated negative associations of drought severity with same-year mortality could be consistent with either a protective effect of drought on total mortality in the same year, or with a delayed health effect of drought beyond the same year. Further research is needed to clarify associations of drought with more specific causes of death and with sublethal health outcomes, for specific subpopulations, and considering lagged effects occurring beyond the same year as the drought.

Calibrated Bayesian Credible Intervals for Binomial Proportions.

Drawbacks of traditional approximate (Wald test-based) and exact (Clopper-Pearson) confidence intervals for a binomial proportion are well-recognized. Alternatives include an interval based on inverting the score test, adaptations of exact testing, and Bayesian credible intervals derived from uniform or Jeffreys beta priors. We recommend a new interval intermediate between the Clopper-Pearson and Jeffreys in terms of both width and coverage. Our strategy selects a value κ between 0 and 0.5 based on stipulated coverage criteria over a grid of regions comprising the parameter space, and bases lower and upper limits of a credible interval on Beta(κ, 1- κ) and Beta(1- κ, κ) priors, respectively. The result tends toward the Jeffreys interval if the criterion is to ensure an average overall coverage rate (1-α) across a single region of width 1, and toward the Clopper-Pearson if the goal is to constrain both lower and upper lack of coverage rates at α/2 with region widths approaching zero. We suggest an intermediate target that ensures all average lower and upper lack of coverage rates over a specified set of regions are ≤ α/2. Interval width subject to these criteria is readily optimized computationally, and we demonstrate particular benefits in terms of coverage balance.

Logistic regression with a continuous exposure measured in pools and subject to errors.

In a multivariable logistic regression setting where measuring a continuous exposure requires an expensive assay, a design in which the biomarker is measured in pooled samples from multiple subjects can be very cost effective. A logistic regression model for poolwise data is available, but validity requires that the assay yields the precise mean exposure for members of each pool. To account for errors, we assume the assay returns the true mean exposure plus a measurement error (ME) and/or a processing error (PE). We pursue likelihood-based inference for a binary health-related outcome modeled by logistic regression coupled with a normal linear model relating individual-level exposure to covariates and assuming that the ME and PE components are independent and normally distributed regardless of pool size. We compare this approach with a discriminant function-based alternative, and we demonstrate the potential value of incorporating replicates into the study design. Applied to a reproductive health dataset with pools of size 2 along with individual samples and replicates, the model fit with both ME and PE had a lower AIC than a model accounting for ME only. Relative to ignoring errors, this model suggested a somewhat higher (though still nonsignificant) adjusted log-odds ratio associating the cytokine MCP-1 with risk of spontaneous abortion. Simulations modeled after these data confirm validity of the methods, demonstrate how ME and particularly PE can reduce the efficiency advantage of a pooling design, and highlight the value of replicates in improving stability when both errors are present.

An efficient design strategy for logistic regression using outcome- and covariate-dependent pooling of biospecimens prior to assay.

Potential reductions in laboratory assay costs afforded by pooling equal aliquots of biospecimens have long been recognized in disease surveillance and epidemiological research and, more recently, have motivated design and analytic developments in regression settings. For example, Weinberg and Umbach (1999, Biometrics 55, 718-726) provided methods for fitting set-based logistic regression models to case-control data when a continuous exposure variable (e.g., a biomarker) is assayed on pooled specimens. We focus on improving estimation efficiency by utilizing available subject-specific information at the pool allocation stage. We find that a strategy that we call "(y,c)-pooling," which forms pooling sets of individuals within strata defined jointly by the outcome and other covariates, provides more precise estimation of the risk parameters associated with those covariates than does pooling within strata defined only by the outcome. We review the approach to set-based analysis through offsets developed by Weinberg and Umbach in a recent correction to their original paper. We propose a method for variance estimation under this design and use simulations and a real-data example to illustrate the precision benefits of (y,c)-pooling relative to y-pooling. We also note and illustrate that set-based models permit estimation of covariate interactions with exposure.

Pollen from Arabidopsis thaliana and other Brassicaceae are functionally omniaperturate.

PREMISE OF THE STUDY: Most pollen walls are interrupted by apertures, thin areas providing access to stigmatic fluids and exit points for pollen tubes. Unexpectedly, pollen tubes of Arabidopsis thaliana are not obligated to pass through apertures and can instead take the shortest route into the stigma, passing directly through a nonaperturate wall. METHODS: We used stains and confocal microscopy to follow early pollen tube formation in A. thaliana and 200+ other species. We germinated pollen in vitro and in situ (at control and high humidities) and also used atomic force microscopy to assay material properties of nonaperture and aperture walls. KEY RESULTS: Pollen tubes of A. thaliana breached nonaperture walls despite these being an order of magnitude stiffer than aperture walls. Breakout was associated with localized swelling of the pectin-rich (alcian blue positive) intine. The precision of pollen tube exit at the pollen-stigma interface was lost at high humidity. Pollen from ∼4% of the species surveyed exhibited breakout germination behavior; all nine breakout species identified so far are in the Brassicaceae family (∼25% of the Brassicaceae sampled) and are scattered across seven tribes. CONCLUSIONS: The polarity of pollen germination in A. thaliana is externally induced, not linked to aperture location. The biomechanical force for breaking nonaperture walls is found in localized swelling of intine pectins. As such, the pollen from A. thaliana, and likely many Brassicaceae family members, are functionally omniaperturate. This new mechanism for germination between extant apertures raises questions about exine porosity and the diversity of mechanisms across taxa.

Serum polybrominated diphenyl ether concentrations and thyroid function in young children.

Thyroid hormones are essential for proper neurodevelopment in early life. There is evidence that exposure to polybrominated diphenyl ethers (PBDEs) affects thyroid function, but previous studies have been inconsistent, and no studies among children have been conducted in the United States where PBDE levels are particularly high. Serum levels of seven PBDE congeners and thyroid hormones and other thyroid parameters were measured in 80 children aged 1-5 years from the southeastern United States between 2011 and 2012. Parents of the children completed questionnaires with details on demographics and behaviors. Multivariate linear regression models were used to estimate the associations between serum PBDE levels, expressed as quartiles and as log-transformed continuous variables, and markers of thyroid function. BDE-47, 99, 100 and 153 were detected in >60% of samples, and were summed (∑PBDE). PBDE congeners and ∑PBDE were positively associated with thyroid-stimulating hormone (TSH). A log-unit increase in ∑PBDE was associated with a 22.1% increase in TSH (95% CI: 2.0%, 47.7%). Compared with children in the lowest quartile of ∑PBDE exposure, children in higher quartiles had greater TSH concentrations as modeled on the log-scale (second quartile: ß=0.32, 95% confidence interval (CI): -0.09, 0.74; third quartile: ß=0.44, 95% CI: 0.04, 0.85; and fourth quartile: ß=0.49, 95% CI: 0.09, 0.89). There was also a tendency toward lower total T4 and higher free T3 with increasing PBDE exposure. Results suggest that exposure to PBDEs during childhood subclinically disrupts thyroid hormone function, with impacts in the direction of hypothyroidism.

Case-control data analysis for randomly pooled biomarkers.

Pooled study designs, where individual biospecimens are combined prior to measurement via a laboratory assay, can reduce lab costs while maintaining statistical efficiency. Analysis of the resulting pooled measurements, however, often requires specialized techniques. Existing methods can effectively estimate the relation between a binary outcome and a continuous pooled exposure when pools are matched on disease status. When pools are of mixed disease status, however, the existing methods may not be applicable. By exploiting characteristics of the gamma distribution, we propose a flexible method for estimating odds ratios from pooled measurements of mixed and matched status. We use simulation studies to compare consistency and efficiency of risk effect estimates from our proposed methods to existing methods. We then demonstrate the efficacy of our method applied to an analysis of pregnancy outcomes and pooled cytokine concentrations. Our proposed approach contributes to the toolkit of available methods for analyzing odds ratios of a pooled exposure, without restricting pools to be matched on a specific outcome.

Semiparametric regression models for a right-skewed outcome subject to pooling.

Pooling specimens prior to performing laboratory assays has various benefits. Pooling can help to reduce cost, preserve irreplaceable specimens, meet minimal volume requirements for certain lab tests, and even reduce information loss when a limit of detection is present. Regardless of the motivation for pooling, appropriate analytical techniques must be applied in order to obtain valid inference from composite specimens. When biomarkers are treated as the outcome in a regression model, techniques applicable to individually measured specimens may not be valid when measurements are taken from pooled specimens, particularly when the biomarker is positive and right skewed. In this paper, we propose a novel semiparametric estimation method based on an adaptation of the quasi-likelihood approach that can be applied to a right-skewed outcome subject to pooling. We use simulation studies to compare this method with an existing estimation technique that provides valid estimates only when pools are formed from specimens with identical predictor values. Simulation results and analysis of a motivating example demonstrate that, when appropriate estimation techniques are applied to strategically formed pools, valid and efficient estimation of the regression coefficients can be achieved.

Tetrahydrobiopterin lowers muscle sympathetic nerve activity and improves augmentation index in patients with chronic kidney disease.

Chronic kidney disease (CKD) is characterized by overactivation of the sympathetic nervous system (SNS) that contributes to cardiovascular risk. Decreased nitric oxide (NO) bioavailability is a major factor contributing to SNS overactivity in CKD, since reduced neuronal NO leads to increased central SNS activity. Tetrahydrobiopterin (BH4) is an essential cofactor for nitric oxide synthase that increases NO bioavailability in experimental models of CKD. We conducted a randomized, double-blinded, placebo-controlled trial testing the benefits of oral sapropterin dihydrochloride (6R-BH4, a synthetic form of BH4) in CKD. 36 patients with CKD and hypertension were randomized to 12 wk of 1) 200 mg 6R-BH4 twice daily + 1 mg folic acid once daily; vs. 2) placebo + folic acid. The primary endpoint was a change in resting muscle sympathetic nerve activity (MSNA). Secondary endpoints included arterial stiffness using pulse wave velocity (PWV) and augmentation index (AIx), endothelial function using brachial artery flow-mediated dilation and endothelial progenitor cells, endothelium-independent vasodilatation (EID), microalbuminuria, and blood pressure. We observed a significant reduction in MSNA after 12 wk of 6R-BH4 (-7.5 ± 2.1 bursts/min vs. +3.2 ± 1.3 bursts/min; P = 0.003). We also observed a significant improvement in AIx (by -5.8 ± 2.0% vs. +1.8 ± 1.7 in the placebo group, P = 0.007). EID increased significantly (by +2.0 ± 0.59%; P = 0.004) in the 6R-BH4 group, but there was no change in endothelial function. There was a trend toward a reduction in diastolic blood pressure by -4 ± 3 mmHg at 12 wk with 6R-BH4 (P = 0.055). 6R-BH4 treatment may have beneficial effects on SNS activity and central pulse wave reflections in hypertensive patients with CKD.

Accounting for informatively missing data in logistic regression by means of reassessment sampling.

We explore the 'reassessment' design in a logistic regression setting, where a second wave of sampling is applied to recover a portion of the missing data on a binary exposure and/or outcome variable. We construct a joint likelihood function based on the original model of interest and a model for the missing data mechanism, with emphasis on non-ignorable missingness. The estimation is carried out by numerical maximization of the joint likelihood function with close approximation of the accompanying Hessian matrix, using sharable programs that take advantage of general optimization routines in standard software. We show how likelihood ratio tests can be used for model selection and how they facilitate direct hypothesis testing for whether missingness is at random. Examples and simulations are presented to demonstrate the performance of the proposed method.