Incidence and Predictors of Breakthrough and Severe Breakthrough Infections of SARS-CoV-2 After Primary Series Vaccination in Adults: A Population-Based Survey of 22 575 Participants.

BACKGROUND: Breakthrough infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are well documented. The current study estimates breakthrough incidence across pandemic waves, and evaluates predictors of breakthrough and severe breakthrough infections (defined as those requiring hospitalization). METHODS: In total, 89 762 participants underwent longitudinal antibody surveillance. Incidence rates were calculated using total person-days contributed. Bias-corrected and age-adjusted logistic regression determined multivariable predictors of breakthrough and severe breakthrough infection, respectively. RESULTS: The incidence was 0.45 (95% confidence interval [CI], .38-.50) during pre-Delta, 2.80 (95% CI, 2.25-3.14) during Delta, and 11.2 (95% CI, 8.80-12.95) during Omicron, per 10 000 person-days. Factors associated with elevated odds of breakthrough included Hispanic ethnicity (vs non-Hispanic white, OR = 1.243; 95% CI, 1.073-1.441), larger household size (OR = 1.251 [95% CI, 1.048-1.494] for 3-5 vs 1 and OR = 1.726 [95% CI, 1.317-2.262] for more than 5 vs 1 person), rural versus urban living (OR = 1.383; 95% CI, 1.122-1.704), receiving Pfizer or Johnson & Johnson versus Moderna, and multiple comorbidities. Of the 1700 breakthrough infections, 1665 reported on severity; 112 (6.73%) were severe. Higher body mass index, Hispanic ethnicity, vaccine type, asthma, and hypertension predicted severe breakthroughs. CONCLUSIONS: Breakthrough infection was 4-25 times more common during the Omicron-dominant wave versus earlier waves. Higher burden of severe breakthrough infections was identified in subgroups.

Antibody Duration After Infection From SARS-CoV-2 in the Texas Coronavirus Antibody Response Survey.

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).

Long-term immune response to SARS-CoV-2 infection and vaccination in children and adolescents.

BACKGROUND: This analysis examined the durability of antibodies present after SARS-CoV-2 infection and vaccination in children and adolescents. METHODS: Data were collected over 4 time points between October 2020-November 2022 as part of a prospective population-based cohort aged 5-to-19 years (N = 810). Results of the (1) Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test); and (2) qualitative and semi-quantitative detection of antibodies to the SARS CoV-2 spike protein receptor binding domain (Roche S-test); and (3) self-reported antigen/PCR COVID-19 test results, vaccination and symptom status were analyzed. RESULTS: N antibody levels reached a median of 84.10 U/ml (IQR: 20.2, 157.7) cutoff index (COI) ~ 6 months post-infection and increased slightly to a median of 85.25 (IQR: 28.0, 143.0) COI at 12 months post-infection. Peak S antibody levels were reached at a median of 2500 U/mL ~6 months post-vaccination and remained for ~12 months (mean 11.6 months, SD 1.20). CONCLUSIONS: This analysis provides evidence of robust durability of nucleocapsid and spike antibodies in a large pediatric sample up to 12 months post-infection/vaccination. This information can inform pediatric SARS-CoV-2 vaccination schedules. IMPACT: This study provided evidence of robust durability of both nucleocapsid and spike antibodies in a large pediatric sample up to 12 months after infection. Little is known about the long-term durability of natural and vaccine-induced SARS-CoV-2 antibodies in the pediatric population. Here, we determined the durability of anti-SARS-CoV-2 spike (S-test) and nucleocapsid protein (N-test) in children/adolescents after SARS-CoV-2 infection and/or vaccination lasts at least up to 12 months. This information can inform future SARS-CoV-2 vaccination schedules in this age group.

Concurrent and Concordant Oral and Genital High-Risk Human Papillomavirus in the United States: Results from the National Health and Nutrition Examination Survey.

BACKGROUND: Oropharyngeal cancers associated with high-risk human papillomavirus (HR-HPV) infection are increasing in the United States, especially among men. We evaluated the prevalence and predictors of concurrent (genital and oral) and concordant (same-type) HR-HPV infections in the United States. METHODS: We used the National Health and Nutrition Examination Survey from 2009 to 2016. Predictors were assessed via multivariable logistic regression. RESULTS: Among 10 334 respondents, 172 (2.1%) had concurrent infections (109 [3.5%] men and 63 [0.76%] women]. Ninety-three (1.0%) had concordant infections (54 [1.6%] men and 39 [0.5%] women). Predictors of concurrence in men included the following: no longer married versus married (odds ratio [OR], 2.3; 95% confidence interval [CI], 1.3-4.9), living with a partner versus married (3.0; 1.2-7.5), and having 2-5 lifetime oral sex partners (3.0; 1.2-7.5). In women they included the following: no longer married versus married (3.6; 1.3-10.3), ≥2 recent sex partners (4.6; 1.4-15.6 for 2-5 partners and 3.9; 1.1-14.3 for ≥6 partners), and marijuana use (2.2; 1.0-4.5). The predictor of concordance in men and women was no longer married versus married (3.5; 1.2-9.9 in men and 3.2; 1.1-9.4 in women). CONCLUSIONS: Concurrent and concordant HR-HPV infections occur at a high rate, especially among men, and are associated with behavioral factors. This underscores the importance of HPV vaccination, screening, and education in men.

A machine learning compatible method for ordinal propensity score stratification and matching.

Although machine learning techniques that estimate propensity scores for observational studies with multivalued treatments have advanced rapidly in recent years, the development of propensity score adjustment techniques has not kept pace. While machine learning propensity models provide numerous benefits, they do not produce a single variable balancing score that can be used for propensity score stratification and matching. This issue motivates the development of a flexible ordinal propensity scoring methodology that does not require parametric assumptions for the propensity model. The proposed method fits a one-parameter power function to the cumulative distribution function (CDF) of the generalized propensity score (GPS) vector resulting from any machine learning propensity model, and is henceforth called the GPS-CDF method. The estimated parameter from the GPS-CDF method, ã , is a scalar balancing score that can be used to group similar subjects in outcome analyses. Specifically, subjects who received different levels of the treatment are stratified or matched based on their ã value to produce unbiased estimates of the average treatment effect (ATE). Simulation studies presented show remediation of covariate balance, minimal bias in ATEs, and maintain coverage probability. The proposed method is applied to the Mexican-American Tobacco use in Children (MATCh) study to determine whether an ordinal treatment of exposure to smoking imagery in movies causes cigarette experimentation in Mexican-American adolescents.

Propensity score stratification methods for continuous treatments.

Continuous treatments propensity scoring remains understudied as the majority of methods are focused on the binary treatment setting. Current propensity score methods for continuous treatments typically rely on weighting in order to produce causal estimates. It has been shown that in some continuous treatment settings, weighting methods can result in worse covariate balance than had no adjustments been made to the data. Furthermore, weighting is not always stable, and resultant estimates may be unreliable due to extreme weights. These issues motivate the current development of novel propensity score stratification techniques to be used with continuous treatments. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) and the nonparametric GPS-CDF approaches are introduced. Empirical CDFs are used to stratify subjects based on pretreatment confounders in order to produce causal estimates. A detailed simulation study shows superiority of these new stratification methods based on the empirical CDF, when compared with standard weighting techniques. The proposed methods are applied to the "Mexican-American Tobacco use in Children" study to determine the causal relationship between continuous exposure to smoking imagery in movies, and smoking behavior among Mexican-American adolescents. These promising results provide investigators with new options for implementing continuous treatment propensity scoring.

Elicitation of prior probability distributions for a proposed Bayesian randomized clinical trial of whole blood for trauma resuscitation.

BACKGROUND: Whole blood trauma resuscitation is conceptually appealing and increasingly used but lacks evidence. A randomized controlled trial is needed but challenging to design. A Bayesian approach might be more efficient and more interpretable than a conventional frequentist design. We report the results on an elicitation meeting to create prior probability distributions to help develop such a trial. METHODS: In-person expert elicitation meeting, based on Sheffield Elicitation Framework methodology. We used an interactive graphical tool to elicit the quantities of interest (24-hour mortality and certainty required). Two rounds were conducted, with an intervening discussion of deidentified responses. Individual responses were aggregated into probability distributions. RESULTS: Fifteen experts participated. The pooled belief was that the median 24-hour mortality of trauma patients with hemorrhagic shock treated with component therapy (the current standard of care) was 19% (95% credible interval [CrI], 6%-45%), and the median 24-hour mortality of those treated with whole blood, 16% (95% CrI, 5%-39%). The pooled prior distribution for the relative risk had a median of 0.84 (95% CrI, 0.26-3.1), indicating that the expert group had a 64% prior belief that whole blood decreases 24-hour mortality compared to component therapy. CONCLUSIONS: Experts had moderately strong beliefs that whole blood reduces the 24-hour mortality of trauma patients with hemorrhagic shock. These data will assist with the design and planning of a Bayesian trial of whole blood resuscitation, which will help to answer a key question in contemporary transfusion practice.

A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.

Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from "big data" are lacking-the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.

Intent-to-treat analysis of cluster randomized trials when clusters report unidentifiable outcome proportions.

BACKGROUND: Cluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, "The Randomized Recruitment Intervention Trial (RECRUIT)," designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208). METHODS: The novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented. RESULTS: Simulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach. CONCLUSION: The article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.

Vasopressor treatment and mortality following nontraumatic subarachnoid hemorrhage: a nationwide electronic health record analysis.

OBJECTIVE: Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular condition, not only due to the effect of initial hemorrhage, but also due to the complication of delayed cerebral ischemia (DCI). While hypertension facilitated by vasopressors is often initiated to prevent DCI, which vasopressor is most effective in improving outcomes is not known. The objective of this study was to determine associations between initial vasopressor choice and mortality in patients with nontraumatic SAH. METHODS: The authors conducted a retrospective cohort study using a large, national electronic medical record data set from 2000-2014 to identify patients with a new diagnosis of nontraumatic SAH (based on ICD-9 codes) who were treated with the vasopressors dopamine, phenylephrine, or norepinephrine. The relationship between the initial choice of vasopressor therapy and the primary outcome, which was defined as in-hospital death or discharge to hospice care, was examined. RESULTS: In total, 2634 patients were identified with nontraumatic SAH who were treated with a vasopressor. In this cohort, the average age was 56.5 years, 63.9% were female, and 36.5% of patients developed the primary outcome. The incidence of the primary outcome was higher in those initially treated with either norepinephrine (47.6%) or dopamine (50.6%) than with phenylephrine (24.5%). After adjusting for possible confounders using propensity score methods, the adjusted OR of the primary outcome was higher with dopamine (OR 2.19, 95% CI 1.70-2.81) and norepinephrine (OR 2.24, 95% CI 1.80-2.80) compared with phenylephrine. Sensitivity analyses using different variable selection procedures, causal inference models, and machine-learning methods confirmed the main findings. CONCLUSIONS: In patients with nontraumatic SAH, phenylephrine was significantly associated with reduced mortality in SAH patients compared to dopamine or norepinephrine. Prospective randomized clinical studies are warranted to confirm this finding.

Global differences in the prevalence of the CpG island methylator phenotype of colorectal cancer.

BACKGROUND: CpG Island Methylator Phenotype (CIMP) is an epigenetic phenotype in CRC characterized by hypermethylation of CpG islands in promoter regions of tumor suppressor genes, leading to their transcriptional silencing and loss of function. While the prevalence of CRC differs across geographical regions, no studies have compared prevalence of CIMP-High phenotype across regions. The purpose of this project was to compare the prevalence of CIMP across geographical regions after adjusting for variations in methodologies to measure CIMP in a meta-analysis. METHODS: We searched PubMed, Medline, and Embase for articles focusing on CIMP published from 2000 to 2018. Two reviewers independently identified 111 articles to be included in final meta-analysis. We classified methods used to quantify CIMP into 4 categories: a) Classical (MINT marker) Panel group b) Weisenberg-Ogino (W-O) group c) Human Methylation Arrays group and d) Miscellaneous group. We compared the prevalence of CIMP across geographical regions after correcting for methodological variations using meta-regression techniques. RESULTS: The pooled prevalence of CIMP-High across all studies was 22% (95% confidence interval:21-24%; I2 = 94.75%). Pooled prevalence of CIMP-H across Asia, Australia, Europe, North America and South America was 22, 21, 21, 27 and 25%, respectively. Meta-regression analysis identified no significant differences in the prevalence of CIMP-H across geographical regions after correction for methodological variations. In exploratory analysis, we observed variations in CIMP-H prevalence across countries. CONCLUSION: Although no differences were found for CIMP-H prevalence across countries, further studies are needed to compare the influence of demographic, lifestyle and environmental factors in relation to the prevalence of CIMP across geographical regions.

Identification and initial validation of empirically derived bipolar symptom states from a large longitudinal dataset: an application of hidden Markov modeling to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study.

BACKGROUND: Although bipolar disorder (BD) is a fundamentally cyclical illness, a divided model of BD that emphasizes polarity over cyclicity has dominated modern psychiatric diagnostic systems since their advent in the 1980s. However, there has been a gradual return to conceptualizations of BD which focus on longitudinal course in the research community due to emerging supportive data. Advances in longitudinal statistical methods promise to further progress the field. METHODS: The current study employed hidden Markov modeling to uncover empirically derived manic and depressive states from longitudinal data [i.e. Young Mania Rating Scale and Montgomery-Asberg Depression Rating Scale responses across five occasions from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study], estimate participants' probabilities of transitioning between these states over time (n = 3918), and evaluate whether clinical variables (e.g. rapid cycling and substance dependence) predict participants' state transitions (n = 3229). RESULTS: Analyses identified three empirically derived mood states ('euthymic,' 'depressed,' and 'mixed'). Relative to the euthymic and depressed states, the mixed state was less commonly experienced, more temporally unstable, and uniquely associated with rapid cycling, substance use, and psychosis. Individuals assigned to the mixed state at baseline were relatively less likely to be diagnosed with BD-II (v. BD-I), more likely to present with a mixed or (hypo)manic episode, and reported experiencing irritable and elevated mood more frequently. CONCLUSIONS: The results from the current study represent an important step in defining, and characterizing the longitudinal course of, empirically derived mood states that can be used to form the foundation of objective, empirical attempts to define meaningful subtypes of affective illness defined by clinical course.

Characterizing red blood cell age exposure in massive transfusion therapy: the scalar age of blood index (SBI).

BACKGROUND: The mortality of trauma patients requiring massive transfusion to treat hemorrhagic shock approaches 17% at 24 hours and 26% at 30 days. The use of stored RBCs is limited to less than 42 days, so older RBCs are delivered first to rapidly bleeding trauma patients. Patients who receive a greater quantity of older RBCs may have a higher risk for mortality. METHODS AND MATERIALS: Characterizing blood age exposure requires accounting for the age of each RBC unit and the quantity of transfused units. To address this challenge, a novel Scalar Age of Blood Index (SBI) that represents the relative distribution of RBCs received is introduced and applied to a secondary analysis of the Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) randomized controlled trial (NCT01545232, https://clinicaltrials.gov/ct2/show/NCT01545232). The effect of the SBI is assessed on the primary PROPPR outcome, 24-hour and 30-day mortality. RESULTS: The distributions of blood storage ages successfully maps to a parameter (SBI) that fully defines the blood age curve for each patient. SBI was a significant predictor of 24-hour and 30-day mortality in an adjusted model that had strong predictive ability (odds ratio, 1.15 [1.01-1.29], p = 0.029, C-statistic, 0.81; odds ratio, 1.14 [1.02-1.28], p = 0.019, C-statistic, 0.88, respectively). CONCLUSION: SBI is a simple scalar metric of blood age that accounts for the relative distribution of RBCs among age categories. Transfusion of older RBCs is associated with 24-hour and 30-day mortality, after adjustment for total units and clinical covariates.

Older Blood Is Associated With Increased Mortality and Adverse Events in Massively Transfused Trauma Patients: Secondary Analysis of the PROPPR Trial.

STUDY OBJECTIVE: The transfusion of older packed RBCs may be harmful in critically ill patients. We seek to determine the association between packed RBC age and mortality among trauma patients requiring massive packed RBC transfusion. METHODS: We analyzed data from the Pragmatic, Randomized Optimal Platelet and Plasma Ratios trial. Subjects in the parent trial included critically injured adult patients admitted to 1 of 12 North American Level I trauma centers who received at least 1 unit of packed RBCs and were predicted to require massive blood transfusion. The primary exposure was volume of packed RBC units transfused during the first 24 hours of hospitalization, stratified by packed RBC age category: 0 to 7 days, 8 to 14 days, 15 to 21 days, and greater than or equal to 22 days. The primary outcome was 24-hour mortality. We evaluated the association between transfused volume of each packed RBC age category and 24-hour survival, using random-effects logistic regression, adjusting for total packed RBC volume, patient age, sex, race, mechanism of injury, Injury Severity Score, Revised Trauma Score, clinical site, and trial treatment group. RESULTS: The 678 patients included in the analysis received a total of 8,830 packed RBC units. One hundred patients (14.8%) died within the first 24 hours. On multivariable analysis, the number of packed RBCs greater than or equal to 22 days old was independently associated with increased 24-hour mortality (adjusted odds ratio [OR] 1.05 per packed RBC unit; 95% confidence interval [CI] 1.01 to 1.08): OR 0.97 for 0 to 7 days old (95% CI 0.88 to 1.08), OR 1.04 for 8 to 14 days old (95% CI 0.99 to 1.09), and OR 1.02 for 15 to 21 days old (95% CI 0.98 to 1.06). Results of sensitivity analyses were similar only among patients who received greater than or equal to 10 packed RBC units. CONCLUSION: Increasing quantities of older packed RBCs are associated with increased likelihood of 24-hour mortality in trauma patients receiving massive packed RBC transfusion (≥10 units), but not in those who receive fewer than 10 units.

Multivariate network meta-analysis to mitigate the effects of outcome reporting bias.

Outcome reporting bias (ORB) is recognized as a threat to the validity of both pairwise and network meta-analysis (NMA). In recent years, multivariate meta-analytic methods have been proposed to reduce the impact of ORB in the pairwise setting. These methods have shown that multivariate meta-analysis can reduce bias and increase efficiency of pooled effect sizes. However, it is unknown whether multivariate NMA (MNMA) can similarly reduce the impact of ORB. Additionally, it is quite challenging to implement MNMA due to the fact that correlation between treatments and outcomes must be modeled; thus, the dimension of the covariance matrix and number of components to estimate grows quickly with the number of treatments and number of outcomes. To determine whether MNMA can reduce the effects of ORB on pooled treatment effect sizes, we present an extensive simulation study of Bayesian MNMA. Via simulation studies, we show that MNMA reduces the bias of pooled effect sizes under a variety of outcome missingness scenarios, including missing at random and missing not at random. Further, MNMA improves the precision of estimates, producing narrower credible intervals. We demonstrate the applicability of the approach via application of MNMA to a multi-treatment systematic review of randomized controlled trials of anti-depressants for the treatment of depression in older adults.

Design of a cluster-randomized minority recruitment trial: RECRUIT.

BACKGROUND: Racial/ethnic minority groups remain underrepresented in clinical trials. Many strategies to increase minority recruitment focus on minority communities and emphasize common diseases such as hypertension. Scant literature focuses on minority recruitment to trials of less common conditions, often conducted in specialty clinics and dependent on physician referrals. We identified trust/mistrust of specialist physician investigators and institutions conducting medical research and consequent participant reluctance to participate in clinical trials as key-shared barriers across racial/ethnic groups. We developed a trust-based continuous quality improvement intervention to build trust between specialist physician investigators and community minority-serving physicians and ultimately potential trial participants. To avoid the inherent biases of non-randomized studies, we evaluated the intervention in the national Randomized Recruitment Intervention Trial (RECRUIT). This report presents the design of RECRUIT. Specialty clinic follow-up continues through April 2017. METHODS: We hypothesized that specialist physician investigators and coordinators trained in the trust-based continuous quality improvement intervention would enroll a greater proportion of minority participants in their specialty clinics than specialist physician investigators in control specialty clinics. Specialty clinic was the unit of randomization. Using continuous quality improvement, the specialist physician investigators and coordinators tailored recruitment approaches to their specialty clinic characteristics and populations. Primary analyses were adjusted for clustering by specialty clinic within parent trial and matching covariates. RESULTS: RECRUIT was implemented in four multi-site clinical trials (parent trials) supported by three National Institutes of Health institutes and included 50 associated specialty clinics from these parent trials. Using current data, we have 88% power or greater to detect a 0.15 or greater difference from the currently observed control proportion adjusting for clustering. We detected no differences in baseline matching criteria between intervention and control specialty clinics (all p values > 0.17). CONCLUSION: RECRUIT was the first multi-site randomized control trial to examine the effectiveness of a trust-based continuous quality improvement intervention to increase minority recruitment into clinical trials. RECRUIT's innovations included its focus on building trust between specialist investigators and minority-serving physicians, the use of continuous quality improvement to tailor the intervention to each specialty clinic's specific racial/ethnic populations and barriers to minority recruitment, and the use of specialty clinics from more than one parent multi-site trial to increase generalizability. The effectiveness of the RECRUIT intervention will be determined after the completion of trial data collection and planned analyses.

Assessing the impact of safety monitoring on the efficacy analysis in large Phase III group sequential trials with non-trivial safety event rate.

In Phase III clinical trials for life-threatening conditions, some serious but expected adverse events, such as early deaths or congestive heart failure, are often treated as the secondary or co-primary endpoint, and are closely monitored by the Data and Safety Monitoring Committee (DSMC). A naïve group sequential design (GSD) for such a study is to specify univariate statistical boundaries for the efficacy and safety endpoints separately, and then implement the two boundaries during the study, even though the two endpoints are typically correlated. One problem with this naïve design, which has been noted in the statistical literature, is the potential loss of power. In this article, we develop an analytical tool to evaluate this negative impact for trials with non-trivial safety event rates, particularly when the safety monitoring is informal. Using a bivariate binary power function for the GSD with a random-effect component to account for subjective decision-making in safety monitoring, we demonstrate how, under common conditions, the power loss in the naïve design can be substantial. This tool may be helpful to entities such as the DSMCs when they wish to deviate from the prespecified stopping boundaries based on safety measures.

Alternative clinical trial design in neurocritical care.

Neurocritical care involves the care of highly complex patients with combinations of physiologic derangements in the brain and in extracranial organs. The level of evidence underpinning treatment recommendations remains low due to a multitude of reasons including an incomplete understanding of the involved physiology; lack of good quality, prospective, standardized data; and the limited success of conventional randomized controlled trials. Comparative effectiveness research can provide alternative perspectives and methods to enhance knowledge and evidence within the field of neurocritical care; these include large international collaborations for generation and maintenance of high quality data, statistical methods that incorporate heterogeneity and individualize outcome prediction, and finally advanced bioinformatics that integrate large amounts of variable-source data into patient-specific phenotypes and trajectories.

Joint model for a diagnostic test without a gold standard in the presence of a dependent terminal event.

Breast cancer patients after breast conservation therapy often develop ipsilateral breast tumor relapse (IBTR), whose classification (true local recurrence versus new ipsilateral primary tumor) is subject to error, and there is no available gold standard. Some patients may die because of breast cancer before IBTR develops. Because this terminal event may be related to the individual patient's unobserved disease status and time to IBTR, the terminal mechanism is non-ignorable. This article presents a joint analysis framework to model the binomial regression with misclassified binary outcome and the correlated time to IBTR, subject to a dependent terminal event and in the absence of a gold standard. Shared random effects are used to link together two survival times. The proposed approach is evaluated by a simulation study and is applied to a breast cancer data set consisting of 4477 breast cancer patients. The proposed joint model can be conveniently fit using adaptive Gaussian quadrature tools implemented in SAS 9.3 (SAS Institute Inc., Cary, NC, USA) procedure NLMIXED.

Baseline characteristics of SARS-CoV-2 vaccine non-responders in a large population-based sample.

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and "other" non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level.