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.

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.

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.

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.

Dynamic borrowing in the presence of treatment effect heterogeneity.

A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered.

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.

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.

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.

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.

An Evaluation of Potential Unintended Consequences of a Nicotine Product Standard: A Focus on Drinking History and Outcomes.

INTRODUCTION: A nicotine product standard reducing the nicotine content in cigarettes could improve public health by reducing smoking. This study evaluated the potential unintended consequences of a reduced nicotine product standard by examining its effects on (1) smoking behaviors based on drinking history; (2) drinking behavior; and (3) daily associations between smoking and drinking. METHODS: Adults who smoke daily (n = 752) in the United States were randomly assigned to smoke very low nicotine content (VLNC) cigarettes versus normal nicotine content (NNC; control) cigarettes for 20 weeks. Linear mixed models determined if baseline drinking moderated the effects of VLNC versus NNC cigarettes on Week 20 smoking outcomes. Time-varying effect models estimated the daily association between smoking VLNC cigarettes and drinking outcomes. RESULTS: Higher baseline alcohol use (vs no use or lower use) was associated with a smaller effect of VLNC on Week 20 urinary total nicotine equivalents (ps < .05). No additional moderation was supported (ps > .05). In the subsample who drank (n = 415), in the VLNC versus NNC condition, daily alcohol use was significantly reduced from Weeks 17 to 20 and odds of binge drinking were significantly reduced from Weeks 9 to 17. By Week 7, in the VLNC cigarette condition (n = 272), smoking no longer predicted alcohol use but remained associated with binge drinking. CONCLUSIONS: We did not support negative unintended consequences of a nicotine product standard. Nicotine reduction in cigarettes generally affected smoking behavior for individuals who do not drink or drink light-to-moderate amounts in similar ways. Extended VLNC cigarette use may improve public health by reducing drinking behavior. IMPLICATIONS: There was no evidence that a VLNC product standard would result in unintended consequences based on drinking history or when considering alcohol outcomes. Specifically, we found that a very low nicotine standard in cigarettes generally reduces smoking outcomes for those who do not drink and those who drink light-to-moderate amounts. Furthermore, an added public health benefit of a very low nicotine standard for cigarettes could be a reduction in alcohol use and binge drinking over time. Finally, smoking VLNC cigarettes may result in a decoupling of the daily associations between smoking and drinking.

Responses to Gradual and Immediate Reduction of Nicotine in Cigarettes in Young Versus Older Adult Smokers.

INTRODUCTION: As the FDA works to determine whether a nicotine reduction policy would benefit public health, one key question is whether to mandate an immediate or gradual reduction in nicotine levels in cigarettes. The aim of this study was to determine whether the effects of gradual versus immediate nicotine reduction on cigarettes per day (CPD), total nicotine equivalents, and subjective responses differed in younger adults versus older adults. METHODS: Using data from a recent randomized trial conducted in the United States (N = 1250) that switched smokers over a 20-week period to very low nicotine content (VLNC) cigarettes either immediately, gradually (via monthly reductions in nicotine content), or not at all (control condition, normal nicotine content research cigarette), we analyzed the moderating effect of age (age 18-24 or 25+). RESULTS: For both age groups, CPD in the immediate condition was significantly lower relative to gradual condition (estimated mean difference of 6.3 CPD in young adults, 5.2 CPD in older adults; p's < .05). Younger and older adults in the immediate and gradual reduction conditions had lower total nicotine equivalents at Week 20 (all p's < .05) than those in the control condition; age group did not moderate this effect. Positive subjective responses to cigarettes were lower among young adults relative to older adults in the immediate condition. CONCLUSIONS: These results indicate that an immediate reduction in nicotine would result in beneficial effects in both young and older adults. Young adults show less positive subjective effects of smoking following switching to VLNC cigarettes relative to older adults. IMPLICATIONS: As researchers work to understand how a potential reduced-nicotine product standard for cigarettes may affect public health, one question is whether nicotine should be reduced immediately or gradually. This study demonstrates that both young and older adults who were switched immediately to the lowest content of nicotine smoked fewer CPD and had lower nicotine intake than those in the gradual condition. Furthermore, young adults appear to show lower positive subjective effects following switching to VLNC cigarettes relative to older adults. This is consistent with previous work demonstrating that young people appear to show lower abuse liability for VLNC cigarettes.

Detecting participant noncompliance across multiple time points by modeling a longitudinal biomarker.

INTRODUCTION: Participant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of randomized clinical trials. No gold standard has been identified for detecting noncompliance, but in some trials participants' biomarkers can provide objective information that suggests exposure to non-study treatments. However, existing methods are limited to retrospectively detecting noncompliance at a single time point based on a single biomarker measurement. We propose a novel method that can leverage participants' full biomarker history to detect noncompliance across multiple time points. Conditional on longitudinal biomarker data, our method can estimate the probability of compliance at (1) a single time point of the trial, (2) all time points, and (3) a future time point. METHODS: Across time points, we model the biomarker as a mixture density with (latent) components corresponding to longitudinal patterns of compliance. To estimate the mixture density, we fit mixed effects models for both compliance and the biomarker. We use the mixture density to derive compliance probabilities that condition on the longitudinal biomarker data. We evaluate our compliance probabilities by simulation and apply them to a trial in which current smokers were asked to only smoke low nicotine study cigarettes (Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2). In the simulation, we investigated three different effects of compliance on the biomarker, as well as the effect of misspecification of the covariance structures. We compared probability estimators (1) and (2) to those that ignore the longitudinal correlation in the data according to area under the receiver operating characteristic curve. We evaluated estimator (3) by plotting its calibration lines. For Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, we compared estimators (1) and (3) to a probability estimator of compliance at the last time point that ignores the longitudinal correlation. RESULTS: In the simulation, for both compliance at the last time point and at all time points, conditioning on the longitudinal biomarker data uniformly raised area under the receiver operating characteristic curve across all three compliance effect scenarios. The gains in area under the receiver operating characteristic curve were smaller under misspecification. The calibration lines for the prediction of compliance closely followed 45°, though with additional variability under misspecification. For compliance at the last time point of Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, conditioning on participants' full biomarker history boosted area under the receiver operating characteristic curve by three percentage points. The prediction probabilities somewhat accurately approximated the non-longitudinal compliance probabilities. DISCUSSION: Compared to existing methods that only use a single biomarker measurement, our method can account for the longitudinal correlation in the biomarker and compliance to more accurately identify noncompliant participants. Our method can also use participants' biomarker history to predict compliance at a future time point.

Calibrated dynamic borrowing using capping priors.

Multisource exchangeability models (MEMs), a BayeTsian approach for dynamically integrating information from multiple clinical trials, are a promising approach for gaining efficiency in randomized controlled trials. When the supplementary trials are considerably larger than the primary trial, care must be taken when integrating supplementary data to avoid overwhelming the primary trial. In this paper, we propose "capping priors," which controls the extent of dynamic borrowing by placing an a priori cap on the effective supplemental sample size. We demonstrate the behavior of this technique via simulation, and apply our method to four randomized trials of very low nicotine content cigarettes.

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.

A fully Bayesian mixture model approach for identifying noncompliance in a regulatory tobacco clinical trial.

Identifying noncompliance in a randomized trial is challenging, but could be improved by leveraging biomarker data to identify participants that did not comply with their assigned treatment. For randomized trials of very low nicotine content (VLNC) cigarettes, the biomarker of total nicotine equivalents (TNE) could be used to identify noncompliance. Compliant participants should have lower levels of TNEs than participants that did not comply and smoked normal nicotine content cigarettes, resulting in a mixture of compliant and noncompliant participants at each dose level. Thresholds of TNE could then be identified from the compliant groups at each dose level and used to determine which study participants were compliant. Furthermore, proposed biological relationships of TNE with nicotine dose could be incorporated into improve the efficiency of estimation, but may introduce bias if misspecified. To account for multiple modeling assumptions across dose levels, we explore model averaging via reversible jump markov chain monte carlo (MCMC) within each dose level to take advantage of improvements in efficiency when the proposed relationship is true and to downweight the biological model when it is misspecified. In simulation studies, we demonstrate that model averaging in the presence of a correct biological relationship results in a decrease in the mean square error (MSE) of up to 85%, but downweights the model in dose levels where the relationship is not appropriate. We apply our approach to data from a randomized trial of VLNC cigarettes to estimate TNE thresholds and probability of compliance curves as a function of TNEs for each nicotine dose used in the trial.

A Randomized, Controlled Trial of ZMapp for Ebola Virus Infection.

BACKGROUND: Data from studies in nonhuman primates suggest that the triple monoclonal antibody cocktail ZMapp is a promising immune-based treatment for Ebola virus disease (EVD). METHODS: Beginning in March 2015, we conducted a randomized, controlled trial of ZMapp plus the current standard of care as compared with the current standard of care alone in patients with EVD that was diagnosed in West Africa by polymerase-chain-reaction (PCR) assay. Eligible patients of any age were randomly assigned in a 1:1 ratio to receive either the current standard of care or the current standard of care plus three intravenous infusions of ZMapp (50 mg per kilogram of body weight, administered every third day). Patients were stratified according to baseline PCR cycle-threshold value for the virus (≤22 vs. >22) and country of enrollment. Oral favipiravir was part of the current standard of care in Guinea. The primary end point was mortality at 28 days. RESULTS: A total of 72 patients were enrolled at sites in Liberia, Sierra Leone, Guinea, and the United States. Of the 71 patients who could be evaluated, 21 died, representing an overall case fatality rate of 30%. Death occurred in 13 of 35 patients (37%) who received the current standard of care alone and in 8 of 36 patients (22%) who received the current standard of care plus ZMapp. The observed posterior probability that ZMapp plus the current standard of care was superior to the current standard of care alone was 91.2%, falling short of the prespecified threshold of 97.5%. Frequentist analyses yielded similar results (absolute difference in mortality with ZMapp, -15 percentage points; 95% confidence interval, -36 to 7). Baseline viral load was strongly predictive of both mortality and duration of hospitalization in all age groups. CONCLUSIONS: In this randomized, controlled trial of a putative therapeutic agent for EVD, although the estimated effect of ZMapp appeared to be beneficial, the result did not meet the prespecified statistical threshold for efficacy. (Funded by the National Institute of Allergy and Infectious Diseases and others; PREVAIL II ClinicalTrials.gov number, NCT02363322 .).

Bayesian hierarchical modeling based on multisource exchangeability.

Bayesian hierarchical models produce shrinkage estimators that can be used as the basis for integrating supplementary data into the analysis of a primary data source. Established approaches should be considered limited, however, because posterior estimation either requires prespecification of a shrinkage weight for each source or relies on the data to inform a single parameter, which determines the extent of influence or shrinkage from all sources, risking considerable bias or minimal borrowing. We introduce multisource exchangeability models (MEMs), a general Bayesian approach for integrating multiple, potentially non-exchangeable, supplemental data sources into the analysis of a primary data source. Our proposed modeling framework yields source-specific smoothing parameters that can be estimated in the presence of the data to facilitate a dynamic multi-resolution smoothed estimator that is asymptotically consistent while reducing the dimensionality of the prior space. When compared with competing Bayesian hierarchical modeling strategies, we demonstrate that MEMs achieve approximately 2.2 times larger median effective supplemental sample size when the supplemental data sources are exchangeable as well as a 56% reduction in bias when there is heterogeneity among the supplemental sources. We illustrate the application of MEMs using a recently completed randomized trial of very low nicotine content cigarettes, which resulted in a 30% improvement in efficiency compared with the standard analysis.