Implementing Telestroke in the Inpatient Setting: Identifying Factors for Success.

BACKGROUND: Inpatient telestroke programs have emerged as a solution to provide timely stroke care in underserved areas, but their successful implementation and factors influencing their effectiveness remain underexplored. This study aimed to qualitatively evaluate the perspectives of inpatient clinicians located at spoke hospitals participating in a newly established inpatient telestroke program to identify implementation barriers and facilitators. METHODS: This was a formative evaluation relying on semistructured qualitative interviews with 16 inpatient providers (physicians and nurse practitioners) at 5 spoke sites of a hub-and-spoke inpatient telestroke program. The Integrated-Promoting Action on Research Implementation in Health Services framework guided data analysis, focusing on the innovation, recipients, context, and facilitation aspects of implementation. Interviews were transcribed and coded using thematic analysis. RESULTS: Fifteen themes were identified in the data and mapped to the Integrated-Promoting Action on Research Implementation in Health Services framework. Themes related to the innovation (the telestroke program) included easy access to stroke specialists, the benefits of limiting patient transfers, concerns about duplicating tests, and challenges of timing inpatient telestroke visits and notes to align with discharge workflow. Themes pertaining to recipients (care team members and patients) were communication gaps between teams, concern about the supervision of inpatient telestroke advanced practice providers and challenges with nurse empowerment. With regard to the context (hospital and system factors), providers highlighted familiarity with telehealth technologies as a facilitator to implementing inpatient telestroke, yet highlighted resource limitations in smaller facilities. Facilitation (program implementation) was recognized as crucial for education, standardization, and buy-in. CONCLUSIONS: Understanding barriers and facilitators to implementation is crucial to determining where programmatic changes may need to be made to ensure the success and sustainment of inpatient telestroke services.

Optimal timing for an accelerated interim futility analysis incorporating real world data.

BACKGROUND: Randomized controlled trials include interim monitoring guidelines to stop early for safety, efficacy, or futility. Futility monitoring facilitates re-allocation of limited resources. However, conventional methods for interim futility monitoring require a trial to accrue nearly half of the outcome data to make a reliable early stopping decision, limiting its benefit. As early stopping for futility will not inflate type-I error, these analyses are an appealing venue for incorporating external data to improve efficiency. METHODS: We propose a Bayesian approach to futility monitoring leveraging real world data using Semi-Supervised MIXture Multi-source Exchangeability Models, which accounts for both measured and unmeasured differences between data sources. We implement futility monitoring using predictive probabilities and investigate the optimal timing with respect to the expected sample size under the null hypothesis. Because we only incorporate external data during the interim futility analysis the proposed design is not limited by type-I error inflation. RESULTS: When the external and trial data are exchangeable, the proposed method provides a roughly 70 person reduction in expected sample size under the null. Under scenarios where exchangeability does not hold, our approach still provides a 10-20 person reduction in expected sample size under the null with about 80% power. CONCLUSIONS: External data borrowing in interim futility monitoring is a promising venue to improve trial efficiency without type-I error inflation. Approaches that are acceptable to regulatory authorities and leverage the complementary strengths of real world and trial data are vital to more efficiently allocate limited resources amongst clinical trials.

Neoadjuvant systemic oncolytic vesicular stomatitis virus is safe and may enhance long-term survivorship in dogs with naturally occurring osteosarcoma.

Osteosarcoma is a devastating bone cancer that disproportionally afflicts children, adolescents, and young adults. Standard therapy includes surgical tumor resection combined with multiagent chemotherapy, but many patients still suffer from metastatic disease progression. Neoadjuvant systemic oncolytic virus (OV) therapy has the potential to improve clinical outcomes by targeting primary and metastatic tumor sites and inducing durable antitumor immune responses. Here we describe the first evaluation of neoadjuvant systemic therapy with a clinical-stage recombinant oncolytic vesicular stomatitis virus (VSV), VSV-IFNß-NIS, in naturally occurring cancer, specifically appendicular osteosarcoma in companion dogs. Canine osteosarcoma has a similar natural disease history as its human counterpart. VSV-IFNß-NIS was administered prior to standard of care surgical resection, permitting microscopic and genomic analysis of tumors. Treatment was well-tolerated and a "tail" of long-term survivors (∼35%) was apparent in the VSV-treated group, a greater proportion than observed in two contemporary control cohorts. An increase in tumor inflammation was observed in VSV-treated tumors and RNA-seq analysis showed that all the long-term responders had increased expression of a T cell anchored immune gene cluster. We conclude that neoadjuvant VSV-IFNß-NIS is safe and may increase long-term survivorship in dogs with naturally occurring osteosarcoma, particularly those that exhibit pre-existing antitumor immunity.

Semi-supervised mixture multi-source exchangeability model for leveraging real-world data in clinical trials.

The traditional trial paradigm is often criticized as being slow, inefficient, and costly. Statistical approaches that leverage external trial data have emerged to make trials more efficient by augmenting the sample size. However, these approaches assume that external data are from previously conducted trials, leaving a rich source of untapped real-world data (RWD) that cannot yet be effectively leveraged. We propose a semi-supervised mixture (SS-MIX) multisource exchangeability model (MEM); a flexible, two-step Bayesian approach for incorporating RWD into randomized controlled trial analyses. The first step is a SS-MIX model on a modified propensity score and the second step is a MEM. The first step targets a representative subgroup of individuals from the trial population and the second step avoids borrowing when there are substantial differences in outcomes among the trial sample and the representative observational sample. When comparing the proposed approach to competing borrowing approaches in a simulation study, we find that our approach borrows efficiently when the trial and RWD are consistent, while mitigating bias when the trial and external data differ on either measured or unmeasured covariates. We illustrate the proposed approach with an application to a randomized controlled trial investigating intravenous hyperimmune immunoglobulin in hospitalized patients with influenza, while leveraging data from an external observational study to supplement a subgroup analysis by influenza subtype.

Evaluating Human Papillomavirus eHealth in Hmong Adolescents to Promote Vaccinations: Pilot Feasibility Study.

BACKGROUND: Human papillomavirus (HPV) is a common sexually transmitted infection, causing multiple cancers, including cervical, penile, and anal. Infection and subsequent health risks caused by HPV can be diminished by HPV vaccination. Unfortunately, vaccination rates among Hmong Americans are substantially lower than those among other racial and ethnic groups, despite having higher cervical cancer rates than non-Hispanic White women. Such disparities and sparse literature highlight the need for innovative and culturally appropriate educational interventions to improve HPV vaccine rates in Hmong Americans. OBJECTIVE: We aimed to develop and evaluate the effectiveness and usability of an innovative web-based eHealth educational website, the Hmong Promoting Vaccines website (HmongHPV website), for Hmong-American parents and adolescents to improve their knowledge, self-efficacy, and decision-making capacities to obtain HPV vaccinations. METHODS: Through social cognitive theory and community-based participatory action research process, we created a theory-driven and culturally and linguistically appropriate website for Hmong parents and adolescents. We conducted a pre-post intervention pilot study to assess the website's effectiveness and usability. Overall, 30 Hmong-American parent and adolescent dyads responded to questions about HPV and HPV vaccine knowledge, self-efficacy, and decision-making at preintervention, 1 week after intervention, and at the 5-week follow-up. Participants responded to survey questions about website content and processes at 1 and 5 weeks, and a subset of 20 dyad participants participated in telephone interviews 6 weeks later. We used paired t tests (2-tailed) to measure the change in knowledge, self-efficacy, and decision-making processes, and used template analysis to identify a priori themes for website usability. RESULTS: Participants' HPV and HPV vaccine knowledge improved significantly from pre- to postintervention stage and follow-up. Knowledge scores increased from preintervention to 1 week after intervention for both parents (HPV knowledge, P=.01; vaccine knowledge, P=.01) and children (HPV knowledge, P=.01; vaccine knowledge, P<.001), which were sustained at the 5-week follow-up. Parents' average self-efficacy score increased from 21.6 at baseline to 23.9 (P=.007) at post intervention and 23.5 (P=.054) at follow-up. Similar improvements were observed in the teenagers' self-efficacy scores (from 30.3 at baseline to 35.6, P=.009, at post intervention and 35.9, P=.006, at follow-up). Collaborative decision-making between parents and adolescents improved immediately after using the website (P=.002) and at follow-up (P=.02). The interview data also revealed that the website's content was informative and engaging; in particular, participants enjoyed the web-based quizzes and vaccine reminders. CONCLUSIONS: This theory-driven, community-based participatory action research-designed and culturally and linguistically appropriate educational website was well received. It improved Hmong parents' and adolescents' knowledge, self-efficacy, and decision-making processes regarding HPV vaccination. Future studies should examine the website's impact on HPV vaccine uptake and its potential for broader use across various settings (eg, clinics and schools).

Neoadjuvant systemic oncolytic vesicular stomatitis virus is safe and may enhance long-term survivorship in dogs with naturally occurring osteosarcoma.

Osteosarcoma is a devastating bone cancer that disproportionally afflicts children, adolescents, and young adults. Standard therapy includes surgical tumor resection combined with multiagent chemotherapy, but many patients still suffer from metastatic disease progression. Neoadjuvant systemic oncolytic virus (OV) therapy has the potential to improve clinical outcomes by targeting primary and metastatic tumor sites and inducing durable antitumor immune responses. Here we described the first evaluation of neoadjuvant systemic therapy with a clinical-stage recombinant oncolytic Vesicular stomatitis virus (VSV), VSV-IFNß-NIS, in naturally occurring cancer, specifically appendicular osteosarcoma in companion dogs. Canine osteosarcoma has a similar natural disease history as its human counterpart. VSV-IFNß-NIS was administered prior to standard of care surgical resection, permitting microscopic and genomic analysis of tumors. Treatment was well-tolerated and a 'tail' of long-term survivors (~35%) was apparent in the VSV-treated group, a greater proportion than observed in two contemporary control cohorts. An increase in tumor inflammation was observed in VSV-treated tumors and RNAseq analysis showed that all the long-term responders had increased expression of a T-cell anchored immune gene cluster. We conclude that neoadjuvant VSV-IFNß-NIS is safe and may increase long-term survivorship in dogs with naturally occurring osteosarcoma, particularly those that exhibit pre-existing antitumor immunity.

Practical guidance on modeling choices for the virtual twins method.

Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable methods to identify subgroups that respond to the treatment differently from the population average are necessary to achieving this goal. The Virtual Twins (VT) method is a highly cited and implemented method for subgroup identification because of its intuitive framework. However, since its initial publication, many researchers still rely heavily on the authors' initial modeling suggestions without examining newer and more powerful alternatives. This leaves much of the potential of the method untapped. We comprehensively evaluate the performance of VT with different combinations of methods in each of its component steps, under a collection of linear and nonlinear problem settings. Our simulations show that the method choice for Step 1 of VT, in which dense models with high predictive performance are fit for the potential outcomes, is highly influential in the overall accuracy of the method, and Superlearner is a promising choice. We illustrate our findings by using VT to identify subgroups with heterogeneous treatment effects in a randomized, double-blind trial of very low nicotine content cigarettes.

Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI.

Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise PCa classification models are the gold standard, few if any approaches have incorporated the inherent structure of the mpMRI data, such as spatial heterogeneity and between-voxel correlation, into PCa classification. We propose a machine learning-based method to fill in this gap. Our method uses an ensemble learning approach to capture regional heterogeneity in the data, where classifiers are developed at multiple resolutions and combined using the super learner algorithm, and further account for between-voxel correlation through a Gaussian kernel smoother. It allows any type of classifier to be the base learner and can be extended to further classify PCa sub-categories. We introduce the algorithms for binary PCa classification, as well as for classifying the ordinal clinical significance of PCa for which a weighted likelihood approach is implemented to improve the detection of less prevalent cancer categories. The proposed method has shown important advantages over conventional modeling and machine learning approaches in simulations and application to our motivating patient data.

A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI.

Spatial partitioning methods correct for nonstationarity in spatially related data by partitioning the space into regions of local stationarity. Existing spatial partitioning methods can only estimate linear partitioning boundaries. This is inadequate for detecting an arbitrarily shaped anomalous spatial region within a larger area. We propose a novel Bayesian functional spatial partitioning (BFSP) algorithm, which estimates closed curves that act as partitioning boundaries around anomalous regions of data with a distinct distribution or spatial process. Our method utilizes transitions between a fixed Cartesian and moving polar coordinate system to model the smooth boundary curves using functional estimation tools. Using adaptive Metropolis-Hastings, the BFSP algorithm simultaneously estimates the partitioning boundary and the parameters of the spatial distributions within each region. Through simulation we show that our method is robust to shape of the target zone and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using magnetic resonance imaging.

Preliminary evidence on cigarette nicotine reduction with concurrent access to an e-cigarette: Manipulating cigarette nicotine content, e-liquid nicotine content, and e-liquid flavor availability.

The reinforcing characteristics of e-cigarettes could moderate the impact of reducing cigarette nicotine content. In this study, people who smoke daily were recruited from North Carolina and Pennsylvania (US) in 2018 and 2019. Within a randomized 2 × 2 × 2 factorial design, participants received investigational cigarettes and an e-cigarette for 12 weeks. Cigarette nicotine content was very low (0.4 mg/g of tobacco; VLNC) or normal (15.8 mg/g; NNC). E-liquids were 0.3% ("low") or 1.8% ("moderate") freebase nicotine, and available in tobacco flavors or tobacco, fruit, dessert and mint flavors. Study recruitment concluded before reaching the planned sample size (N = 480). Fifty participants were randomized and 32 completed the study. We found that randomization to VLNC, relative to NNC cigarettes, reduced self-reported cigarettes per day (CPD; mean difference: -12.96; 95% CI: -21.51, -4.41; p = 0.005); whereas e-liquid nicotine content and flavor availability did not have significant effects. The effect of cigarette nicotine content was larger in the moderate vs. low nicotine e-liquid groups and in the all flavors versus tobacco flavors e-liquid groups; tests of the interaction between e-liquid characteristics and cigarette nicotine content were not significant. Biomarkers of smoke exposure at Week 12 did not differ across conditions, which may reflect variability in adherence to only using VLNC cigarettes. In conclusion this study offers preliminary evidence that the extent to which cigarette nicotine reduction decreases smoking may depend on the reinforcing characteristics of alternative products, including the available nicotine contents and flavors of e-cigarettes.

Early Changes in Puffing Intensity When Exclusively Using Open-Label Very Low Nicotine Content Cigarettes.

INTRODUCTION: In response to reducing cigarette nicotine content, people who smoke could attempt to compensate by using more cigarettes or by puffing on individual cigarettes with greater intensity. Such behaviors may be especially likely under conditions where normal nicotine content (NNC) cigarettes are not readily accessible. The current within-subject, residential study investigated whether puffing intensity increased with very low nicotine content (VLNC) cigarette use, relative to NNC cigarette use, when no other nicotine products were available. AIMS AND METHODS: Sixteen adults who smoke daily completed two four-night hotel stays in Charleston, South Carolina (United States) in 2018 during which only NNC or only VLNC cigarettes were accessible. We collected the filters from all smoked cigarettes and measured the deposited solanesol to estimate mouth-level nicotine delivery per cigarette. These estimates were averaged within and across participants, per each 24-h period. We then compared the ratio of participant-smoked VLNC and NNC cigarette mouth-level nicotine with the ratio yielded by cigarette smoking machines (when puffing intensity is constant). RESULTS: Average mouth-level nicotine estimates from cigarettes smoked during the hotel stays indicate participants puffed VLNC cigarettes with greater intensity than NNC cigarettes in each respective 24-h period. However, this effect diminished over time (p < .001). Specifically, VLNC puffing intensity was 40.0% (95% CI: 29.9, 53.0) greater than NNC puffing intensity in the first period, and 16.1% (95% CI: 6.9, 26.0) greater in the fourth period. CONCLUSION: Average puffing intensity per cigarette was elevated with exclusive VLNC cigarette use, but the extent of this effect declined across four days. IMPLICATIONS: In an environment where no other sources of nicotine are available, people who smoke daily may initially attempt to compensate for cigarette nicotine reduction by puffing on individual cigarettes with greater intensity. Ultimately, the compensatory behavior changes required to achieve usual nicotine intake from VLNC cigarettes are drastic and unrealistic. Accordingly, people are unlikely to sustain attempts to compensate for very low cigarette nicotine content.

A permutation procedure to detect heterogeneous treatment effects in randomized clinical trials while controlling the type I error rate.

BACKGROUND/AIMS: Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without a priori specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population. METHODS: We propose a permutation procedure for tuning parameter selection that allows for type I error control when testing for heterogeneous treatment effects framed within the Virtual Twins procedure for subgroup identification. We verify that the type I error rate can be controlled at the nominal rate and investigate the power for detecting heterogeneous effects when present through extensive simulation studies. We apply our method to a secondary analysis of data from a randomized trial of very low nicotine content cigarettes. RESULTS: In the absence of type I error control, the observed type I error rate for Virtual Twins was between 99% and 100%. In contrast, models tuned via the proposed permutation were able to control the type I error rate and detect heterogeneous effects when present. An application of our approach to a recently completed trial of very low nicotine content cigarettes identified several variables with potentially heterogeneous treatment effects. CONCLUSIONS: The proposed permutation procedure allows researchers to engage in secondary analyses of clinical trials for treatment effect heterogeneity while maintaining the type I error rate without pre-specifying subgroups.

The Cooked Meat Carcinogen 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine Hair Dosimeter, DNA Adductomics Discovery, and Associations with Prostate Cancer Pathology Biomarkers.

Well-done cooked red meat consumption is linked to aggressive prostate cancer (PC) risk. Identifying mutation-inducing DNA adducts in the prostate genome can advance our understanding of chemicals in meat that may contribute to PC. 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), a heterocyclic aromatic amine (HAA) formed in cooked meat, is a potential human prostate carcinogen. PhIP was measured in the hair of PC patients undergoing prostatectomy, bladder cancer patients under treatment for cystoprostatectomy, and patients treated for benign prostatic hyperplasia (BPH). PhIP hair levels were above the quantification limit in 123 of 205 subjects. When dichotomizing prostate pathology biomarkers, the geometric mean PhIP hair levels were higher in patients with intermediate and elevated-risk prostate-specific antigen values than lower-risk values <4 ng/mL (p = 0.03). PhIP hair levels were also higher in patients with intermediate and high-risk Gleason scores ≥7 compared to lower-risk Gleason score 6 and BPH patients (p = 0.02). PC patients undergoing prostatectomy had higher PhIP hair levels than cystoprostatectomy or BPH patients (p = 0.02). PhIP-DNA adducts were detected in 9.4% of the patients assayed; however, DNA adducts of other carcinogenic HAAs, and benzo[a]pyrene formed in cooked meat, were not detected. Prostate specimens were also screened for 10 oxidative stress-associated lipid peroxidation (LPO) DNA adducts. Acrolein 1,N2-propano-2'-deoxyguanosine adducts were detected in 54.5% of the patients; other LPO adducts were infrequently detected. Acrolein adducts were not associated with prostate pathology biomarkers, although DNA adductomic profiles differed between PC patients with low and high-grade Gleason scores. Many DNA adducts are of unknown origin; however, dG adducts of formaldehyde and a series of purported 4-hydroxy-2-alkenals were detected at higher abundance in a subset of patients with elevated Gleason scores. The PhIP hair biomarker and DNA adductomics data support the paradigm of well-done cooked meat and oxidative stress in aggressive PC risk.

Efficacy of Losartan in Hospitalized Patients With COVID-19-Induced Lung Injury: A Randomized Clinical Trial.

Importance: SARS-CoV-2 viral entry may disrupt angiotensin II (AII) homeostasis, contributing to COVID-19 induced lung injury. AII type 1 receptor blockade mitigates lung injury in preclinical models, although data in humans with COVID-19 remain mixed. Objective: To test the efficacy of losartan to reduce lung injury in hospitalized patients with COVID-19. Design, Setting, and Participants: This blinded, placebo-controlled randomized clinical trial was conducted in 13 hospitals in the United States from April 2020 to February 2021. Hospitalized patients with COVID-19 and a respiratory sequential organ failure assessment score of at least 1 and not already using a renin-angiotensin-aldosterone system (RAAS) inhibitor were eligible for participation. Data were analyzed from April 19 to August 24, 2021. Interventions: Losartan 50 mg orally twice daily vs equivalent placebo for 10 days or until hospital discharge. Main Outcomes and Measures: The primary outcome was the imputed arterial partial pressure of oxygen to fraction of inspired oxygen (Pao2:Fio2) ratio at 7 days. Secondary outcomes included ordinal COVID-19 severity; days without supplemental o2, ventilation, or vasopressors; and mortality. Losartan pharmacokinetics and RAAS components (AII, angiotensin-[1-7] and angiotensin-converting enzymes 1 and 2)] were measured in a subgroup of participants. Results: A total of 205 participants (mean [SD] age, 55.2 [15.7] years; 123 [60.0%] men) were randomized, with 101 participants assigned to losartan and 104 participants assigned to placebo. Compared with placebo, losartan did not significantly affect Pao2:Fio2 ratio at 7 days (difference, -24.8 [95%, -55.6 to 6.1]; P = .12). Compared with placebo, losartan did not improve any secondary clinical outcomes and led to fewer vasopressor-free days than placebo (median [IQR], 9.4 [9.1-9.8] vasopressor-free days vs 8.7 [8.2-9.3] vasopressor-free days). Conclusions and Relevance: This randomized clinical trial found that initiation of orally administered losartan to hospitalized patients with COVID-19 and acute lung injury did not improve Pao2:Fio2 ratio at 7 days. These data may have implications for ongoing clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT04312009.

Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data.

Multi-parametric magnetic resonance imaging (mpMRI) has been playing an increasingly important role in the detection of prostate cancer (PCa). Various computer-aided detection algorithms were proposed for automated PCa detection by combining information in multiple mpMRI parameters. However, there are specific features of mpMRI, including between-voxel correlation within each prostate and heterogeneity across patients, that have not been fully explored but could potentially improve PCa detection if leveraged appropriately. This article proposes novel Bayesian approaches for voxel-wise PCa classification that accounts for spatial correlation and between-patient heterogeneity in the mpMRI data. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we propose three scalable approaches based on Nearest Neighbor Gaussian Process (NNGP), reduced-rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance, respectively. Our simulation study shows that properly modeling the spatial correlation and between-patient heterogeneity can substantially improve PCa classification. Application to in vivo data illustrates that classification is improved by all three spatial modeling approaches considered, while modeling the between-patient heterogeneity does not further improve our classifiers. Among the proposed models, the NNGP-based model is recommended given its high classification accuracy and computational efficiency.

A group-sequential randomized trial design utilizing supplemental trial data.

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

A multi-center phase II randomized clinical trial of losartan on symptomatic outpatients with COVID-19.

BACKGROUND: The SARS-CoV-2 virus enters cells via Angiotensin-converting enzyme 2 (ACE2), disrupting the renin-angiotensin-aldosterone axis, potentially contributing to lung injury. Treatment with angiotensin receptor blockers (ARBs), such as losartan, may mitigate these effects, though induction of ACE2 could increase viral entry, replication, and worsen disease. METHODS: This study represents a placebo-controlled blinded randomized clinical trial (RCT) to test the efficacy of losartan on outpatients with COVID-19 across three hospital systems with numerous community sites in Minnesota, U.S. Participants included symptomatic outpatients with COVID-19 not already taking ACE-inhibitors or ARBs, enrolled within 7 days of symptom onset. Patients were randomized to 1:1 losartan (25 mg orally twice daily unless estimated glomerular filtration rate, eGFR, was reduced, when dosing was reduced to once daily) versus placebo for 10 days, and all patients and outcome assesors were blinded. The primary outcome was all-cause hospitalization within 15 days. Secondary outcomes included functional status, dyspnea, temperature, and viral load. (clinicatrials.gov, NCT04311177, closed to new participants). FINDINGS: From April to November 2020, 117 participants were randomized 58 to losartan and 59 to placebo, and all were analyzed under intent to treat principles. The primary outcome did not differ significantly between the two arms based on Barnard's test [losartan arm: 3 events (5.2% 95% CI 1.1, 14.4%) versus placebo arm: 1 event (1.7%; 95% CI 0.0, 9.1%)]; proportion difference -3.5% (95% CI -13.2, 4.8%); p = 0.32]. Viral loads were not statistically different between treatment groups at any time point. Adverse events per 10 patient days did not differ signifcantly [0.33 (95% CI 0.22-0.49) for losartan vs. 0.37 (95% CI 0.25-0.55) for placebo]. Due to a lower than expected hospitalization rate and low likelihood of a clinically important treatment effect, the trial was terminated early. INTERPRETATION: In this multicenter blinded RCT for outpatients with mild symptomatic COVID-19 disease, losartan did not reduce hospitalizations, though assessment was limited by low event rate. Importantly, viral load was not statistically affected by treatment. This study does not support initiation of losartan for low-risk outpatients.

A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data.

A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers.

Borrowing from supplemental sources to estimate causal effects from a primary data source.

The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression-based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior.

Impact of nicotine reduction in cigarettes on smoking behavior and exposure: Are there differences by race/ethnicity, educational attainment, or gender?

BACKGROUND: Lowering nicotine in cigarettes may reduce smoking prevalences; however, it is not known whether an immediate or gradual reduction in nicotine is the optimal approach for all population groups. OBJECTIVES: We examined whether the optimal approach to nicotine reduction depended on the education, gender, or race of people who smoke and whether the optimal approach differentially benefited people who smoke based on their education, gender, or race. METHODS: Secondary analysis was conducted on a randomized clinical trial (N = 1250) comparing (1) immediate reduction from 15.5 to 0.4 mg of nicotine per gram of tobacco(mg/g);(2) gradual reduction to 0.4 mg/g;(3) control group with normal nicotine cigarettes(15.5 mg/g). Outcomes included cigarettes per day(CPD), carbon monoxide(CO), total nicotine equivalents(TNE), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides(NNAL), phenanthrene tetraol(PheT), N-Acetyl-S-(2-cyanoethyl)-l-cysteine(CEMA). Data were analyzed as area under the curve(AUC). RESULTS: Results were presented by education (High school[HS] or less n = 505, more than HS n = 745), gender (males n = 701, females n = 549), and race (Black participants n = 373,White participants n = 758). Regardless of education, gender, and race, CPD, CO, TNE, NNAL, PheT, and CEMA were lower in immediate versus gradual nicotine reduction. Comparing immediate versus the control, outcomes were lower for all subgroups; however, the magnitude of the effect for TNE varied by race. Specifically, geometric mean of the AUC of TNE in immediate versus gradual was 49 % lower in Black participants and 61 % lower in White participants (p-value = 0.047). CONCLUSIONS: Immediately reducing nicotine in cigarettes has the potential to benefit people who smoke across lower and higher educational attainment, male and female gender, and Black and White race.