Bayesian inference for prediction of survival probability in prime-boost vaccination regimes.

We focus on Bayesian inference for survival probabilities in a prime-boost vaccination regime in the development of an Ebola vaccine. We are interested in the heterologous prime-boost regimen (unmatched vaccine deliverys using the same antigen) due to its demonstrated durable immunity, well-tolerated safety profile, and suitability as a population vaccination strategy. Our research is motivated by the need to estimate the survival probability given the administered dosage. To do so, we establish two key relationships. Firstly, we model the connection between the designed dose concentration and the induced antibody count using a Bayesian response surface model. Secondly, we model the association between the antibody count and the probability of survival when experimental subjects are exposed to the Ebola virus in a controlled setting using a Bayesian probability of survival model. Finally, we employ a combination of the two models with dose concentration as the predictor of the survival probability for a future vaccinated population. We implement our two-level Bayesian model in Stan, and illustrate its use with simulated and real-world data. Performance of this model is evaluated via simulation. Our work offers a new application of drug synergy models to examine prime-boost vaccine efficacy, and does so using a hierarchical Bayesian framework that allows us to use dose concentration to predict survival probability.

QUANTITATIVE ASSESSMENT OF AUTOMATED OPTICAL COHERENCE TOMOGRAPHY IMAGE ANALYSIS USING A HOME-BASED DEVICE FOR SELF-MONITORING NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To evaluate a prototype home optical coherence tomography device and automated analysis software for detection and quantification of retinal fluid relative to manual human grading in a cohort of patients with neovascular age-related macular degeneration. METHODS: Patients undergoing anti-vascular endothelial growth factor therapy were enrolled in this prospective observational study. In 136 optical coherence tomography scans from 70 patients using the prototype home optical coherence tomography device, fluid segmentation was performed using automated analysis software and compared with manual gradings across all retinal fluid types using receiver-operating characteristic curves. The Dice similarity coefficient was used to assess the accuracy of segmentations, and correlation of fluid areas quantified end point agreement. RESULTS: Fluid detection per B-scan had area under the receiver-operating characteristic curves of 0.95, 0.97, and 0.98 for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid, respectively. On a per volume basis, the values for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid were 0.997, 0.998, and 0.998, respectively. The average Dice similarity coefficient values across all B-scans were 0.64, 0.73, and 0.74, and the coefficients of determination were 0.81, 0.93, and 0.97 for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid, respectively. CONCLUSION: Home optical coherence tomography device images assessed using the automated analysis software showed excellent agreement to manual human grading.

Stability analysis using mixed models: A critique of tolerance interval methods and a probabilistic solution.

Recently, tolerance interval approaches to the calculation of a shelf life of a drug product have been proposed in the literature. These address the belief that shelf life should be related to control of a certain proportion of batches being out of specification. We question the appropriateness of the tolerance interval approach. Our concerns relate to the computational challenges and practical interpretations of the method. We provide an alternative Bayesian approach, which directly controls the desired proportion of batches falling out of specification assuming a controlled manufacturing process. The approach has an intuitive interpretation and posterior distributions are straightforward to compute. If prior information on the fixed and random parameters is available, a Bayesian approach can provide additional benefits both to the company and the consumer. It also avoids many of the computational challenges with the tolerance interval methodology.

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA-BIOP Nonclinical Bayesian Working Group.

The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA-BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics.

TOPOGRAPHIC ANALYSIS OF PHOTORECEPTOR LOSS CORRELATED WITH DISEASE MORPHOLOGY IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To quantify morphologic photoreceptor integrity during anti-vascular endothelial growth factor (anti-VEGF) therapy of neovascular age-related macular degeneration and correlate these findings with disease morphology and function. METHODS: This presents a post hoc analysis on spectral-domain optical coherence tomography data of 185 patients, acquired at baseline, Month 3, and Month 12 in a multicenter, prospective trial. Loss of the ellipsoid zone (EZ) was manually quantified in all optical coherence tomography volumes. Intraretinal cystoid fluid, subretinal fluid (SRF), and pigment epithelial detachments were automatically segmented in the full volumes using validated deep learning methods. Spatiotemporal correlation of fluid markers with EZ integrity as well as bivariate analysis between EZ integrity and best-corrected visual acuity was performed. RESULTS: At baseline, EZ integrity was predominantly impaired in the fovea, showing progressive recovery during anti-vascular endothelial growth factor therapy. Topographic analysis at baseline revealed EZ integrity to be more likely intact in areas with SRF and vice versa. Moreover, we observed a correlation between EZ integrity and resolution of SRF. Foveal EZ integrity correlated with best-corrected visual acuity at all timepoints. CONCLUSION: Improvement of EZ integrity during anti-VEGF therapy of neovascular age-related macular degeneration occurred predominantly in the fovea. Photoreceptor integrity correlated with best-corrected visual acuity. Ellipsoid zone integrity was preserved in areas of SRF and showed deterioration upon SRF resolution.

Legacy Contaminants in Aquatic Biota in a Stream Associated with Nuclear Weapons Material Production on the Savannah River Site.

Former nuclear weapons material production at the U.S. Department of Energy's Savannah River Site (SRS) has resulted in contamination of certain terrestrial and aquatic ecosystems on site with legacy wastes such as radiocesium (137Cs), tritium (3H), and metals. We collected fish and invertebrates from five beaver ponds (sites) above, adjacent, and downgradient of three SRS facilities (H-, F-, and C-Areas) to evaluate whether the accumulation of metals and radionuclides in biota were associated with specific facility operations and if the measured levels could pose risks to aquatic organisms. We compared concentrations of various metals, 137Cs, and 3H in fish, as well as in water (3H only), among sites along the stream gradient. Fish collected from sites adjacent to H-Area had significantly higher 137Cs concentrations compared to fish from other sites. Both biota and water samples indicated significantly greater levels of 3H in sites adjacent to and downstream of C-Area. Concentrations of zinc (Zn), copper (Cu), and mercury (Hg) in some samples exceeded effects levels reported for fish and may pose a risk to fish populations. This study reported fish tissue concentrations of 137Cs and 3H, which have not been documented extensively in ecotoxicological studies. Our results suggested that industrial operations such as nuclear material production at SRS could have long-lasting impact on the aquatic ecosystem via the release of radionuclides and metals, and long-term monitoring of physiological effects and population level impact in biota exposed to these contaminants are recommended.

Using accelerated drug stability results to inform long-term studies in shelf life determination.

In the pharmaceutical industry, the shelf life of a drug product is determined by data gathered from stability studies and is intended to provide consumers with a high degree of confidence that the drug retains its strength, quality, and purity under appropriate storage conditions. In this paper, we focus on liquid drug formulations and propose a Bayesian approach to estimate a drug product's shelf life, where prior knowledge gained from the accelerated study conducted during the drug development stage is used to inform the long-term study. Classical and nonlinear Arrhenius regression models are considered for the accelerated conditions, and two examples are given where posterior results from the accelerated study are used to construct priors for a long-term stability study.

Sequestration of U(VI) from Acidic, Alkaline, and High Ionic-Strength Aqueous Media by Functionalized Magnetic Mesoporous Silica Nanoparticles: Capacity and Binding Mechanisms.

Uranium(VI) exhibits little adsorption onto sediment minerals in acidic, alkaline or high ionic-strength aqueous media that often occur in U mining or contaminated sites, which makes U(VI) very mobile and difficult to sequester. In this work, magnetic mesoporous silica nanoparticles (MMSNs) were functionalized with several organic ligands. The functionalized MMSNs were highly effective and had large binding capacity for U sequestration from high salt water (HSW) simulant (54 mg U/g sorbent). The functionalized MMSNs, after U exposure in HSW simulant, pH 3.5 and 9.6 artificial groundwater (AGW), were characterized by a host of spectroscopic methods. Among the key novel findings in this work was that in the HSW simulant or high pH AGW, the dominant U species bound to the functionalized MMSNs were uranyl or uranyl hydroxide, rather than uranyl carbonates as expected. The surface functional groups appear to be out-competing the carbonate ligands associated with the aqueous U species. The uranyl-like species were bound with N ligand as η2 bound motifs or phosphonate ligand as a monodentate, as well as on tetrahedral Si sites as an edge-sharing bidentate. The N and phosphonate ligand-functionalized MMSNs hold promise as effective sorbents for sequestering U from acidic, alkaline or high ionic-strength contaminated aqueous media.

Bayesian assurance and sample size determination in the process validation life-cycle.

Validation of pharmaceutical manufacturing processes is a regulatory requirement and plays a key role in the assurance of drug quality, safety, and efficacy. The FDA guidance on process validation recommends a life-cycle approach which involves process design, qualification, and verification. The European Medicines Agency makes similar recommendations. The main purpose of process validation is to establish scientific evidence that a process is capable of consistently delivering a quality product. A major challenge faced by manufacturers is the determination of the number of batches to be used for the qualification stage. In this article, we present a Bayesian assurance and sample size determination approach where prior process knowledge and data are used to determine the number of batches. An example is presented in which potency uniformity data is evaluated using a process capability metric. By using the posterior predictive distribution, we simulate qualification data and make a decision on the number of batches required for a desired level of assurance.

"A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis".

PURPOSE: Observational studies are frequently used to assess the effectiveness of medical interventions in routine clinical practice. However, the use of observational data for comparative effectiveness is challenged by selection bias and the potential of unmeasured confounding. This is especially problematic for analyses using a health care administrative database, in which key clinical measures are often not available. This paper provides an approach to conducting a sensitivity analyses to investigate the impact of unmeasured confounding in observational studies. METHODS: In a real world osteoporosis comparative effectiveness study, the bone mineral density (BMD) score, an important predictor of fracture risk and a factor in the selection of osteoporosis treatments, is unavailable in the data base and lack of baseline BMD could potentially lead to significant selection bias. We implemented Bayesian twin-regression models, which simultaneously model both the observed outcome and the unobserved unmeasured confounder, using information from external sources. A sensitivity analysis was also conducted to assess the robustness of our conclusions to changes in such external data. RESULTS: The use of Bayesian modeling in this study suggests that the lack of baseline BMD did have a strong impact on the analysis, reversing the direction of the estimated effect (odds ratio of fracture incidence at 24 months: 0.40 vs. 1.36, with/without adjusting for unmeasured baseline BMD). CONCLUSIONS: The Bayesian twin-regression models provide a flexible sensitivity analysis tool to quantitatively assess the impact of unmeasured confounding in observational studies. Copyright © 2016 John Wiley & Sons, Ltd.

Spectroscopic evidence of uranium immobilization in acidic wetlands by natural organic matter and plant roots.

Biogeochemistry of uranium in wetlands plays important roles in U immobilization in storage ponds of U mining and processing facilities but has not been well understood. The objective of this work was to study molecular mechanisms responsible for high U retention by Savannah River Site (SRS) wetland sediments under varying redox and acidic (pH = 2.6-5.8) conditions using U L3-edge X-ray absorption spectroscopy. Uranium in the SRS wetland sediments existed primarily as U(VI) bonded as a bidentate to carboxylic sites (U-C bond distance at ∼2.88 Å), rather than phenolic or other sites of natural organic matter (NOM). In microcosms simulating the SRS wetland processes, U immobilization on roots was 2 orders of magnitude higher than on the adjacent brown or more distant white sands in which U was U(VI). Uranium on the roots were both U(IV) and U(VI), which were bonded as a bidentate to carbon, but the U(VI) may also form a U phosphate mineral. After 140 days of air exposure, all U(IV) was reoxidized to U(VI) but remained as a bidentate bonding to carbon. This study demonstrated NOM and plant roots can highly immobilize U(VI) in the SRS acidic sediments, which has significant implication for the long-term stewardship of U-contaminated wetlands.

Uranium immobilization in an iron-rich rhizosphere of a native wetland plant from the Savannah River Site under reducing conditions.

The hypothesis of this study was that iron plaques formed on the roots of wetland plants and their rhizospheres create environmental conditions favorable for iron reducing bacteria that promote the in situ immobilization of uranium. Greenhouse microcosm studies were conducted using native plants (Sparganium americanum) from a wetland located on the Savannah River Site, Aiken, SC. After iron plaques were established during a 73-day period by using an anoxic Fe(II)-rich nutrient solution, a U(VI) amended nutrient solution was added to the system for an additional two months. Compared to plant-free control microcosms, microcosms containing iron plaques successfully stimulated the growth of targeted iron reducing bacteria, Geobacter spp. Their population continuously increased after the introduction of the U(VI) nutrient solution. The reduction of some of the U(VI) to U(IV) by iron reducing bacteria was deduced based on the observations that the aqueous Fe(II) concentrations increased while the U(VI) concentrations decreased. The Fe(II) produced by the iron reducing bacteria was assumed to be reoxidized by the oxygen released from the roots. Advanced spectroscopic analyses revealed that a significant fraction of the U(VI) had been reduced to U(IV) and they were commonly deposited in association with phosphorus on the iron plaque.

Bayesian modeling of cost-effectiveness studies with unmeasured confounding: a simulation study.

Unmeasured confounding is a common problem in observational studies. Failing to account for unmeasured confounding can result in biased point estimators and poor performance of hypothesis tests and interval estimators. We provide examples of the impacts of unmeasured confounding on cost-effectiveness analyses using observational data along with a Bayesian approach to correct estimation. Assuming validation data are available, we propose a Bayesian approach to correct cost-effectiveness studies for unmeasured confounding. We consider the cases where both cost and effectiveness are assumed to have a normal distribution and when costs are gamma distributed and effectiveness is normally distributed. Simulation studies were conducted to determine the impact of ignoring the unmeasured confounder and to determine the size of the validation data required to obtain valid inferences.

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group.

Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability.

Plasmid size determines adsorption to clay and breakthrough in a saturated sand column.

Horizontal gene transfer (HGT) is a major factor in the spread of antibiotic resistant genes (ARG). Transformation, one mode of HGT, involves the acquisition and expression of extracellular DNA (eDNA). eDNA in soils is degraded rapidly by extracellular nucleases. However, if bound to a clay particle, eDNA can persist for long periods of time without losing its transformation ability. To better understand the mechanism of eDNA persistence in soil, this experiment assessed the effects of 1) clay mineralogy, 2) mixed salt solution, 3) plasmid size on DNA adsorption to clay and 4) breakthrough behavior of three differently sized plasmids in an environmentally relevant solution. Batch test methods were used to determine adsorption trends of three differently sized DNA plasmids, pUC19, pBR322, and pTYB21, to several pure clay minerals, goethite (α-FeOOH), illite, and kaolinite, and one environmental soil sample. Results show not all sorbents have equal adsorption capacity based on surface area with adsorption capacities decreasing from goethite > illite = kaolinite > bulk soil, and low ionic strength solutions will likely not significantly alter sorption trends. Additionally, plasmid DNA size (i.e., length) was shown to be a significant predictor of adsorption efficiency and that size affects DNA breakthrough, with breakthroughs occurring later with larger plasmids. Given that DNA persistence is linked to its adsorption to soil constituents and breakthrough, eDNA size is likely an important contributor to the spread of ARG within natural microbial communities.

Evaluating the impact of unmeasured confounding with internal validation data: an example cost evaluation in type 2 diabetes.

The quantitative assessment of the potential influence of unmeasured confounders in the analysis of observational data is rare, despite reliance on the "no unmeasured confounders" assumption. In a recent comparison of costs of care between two treatments for type 2 diabetes using a health care claims database, propensity score matching was implemented to adjust for selection bias though it was noted that information on baseline glycemic control was not available for the propensity model. Using data from a linked laboratory file, data on this potential "unmeasured confounder" were obtained for a small subset of the original sample. By using this information, we demonstrate how Bayesian modeling, propensity score calibration, and multiple imputation can utilize this additional information to perform sensitivity analyses to quantitatively assess the potential impact of unmeasured confounding. Bayesian regression models were developed to utilize the internal validation data as informative prior distributions for all parameters, retaining information on the correlation between the confounder and other covariates. While assumptions supporting the use of propensity score calibration were not met in this sample, the use of Bayesian modeling and multiple imputation provided consistent results, suggesting that the lack of data on the unmeasured confounder did not have a strong impact on the original analysis, due to the lack of strong correlation between the confounder and the cost outcome variable. Bayesian modeling with informative priors and multiple imputation may be useful tools for unmeasured confounding sensitivity analysis in these situations. Further research to understand the operating characteristics of these methods in a variety of situations, however, remains.

Bayesian sample size determination for a clinical trial with correlated continuous and binary outcomes.

In clinical trials, multiple outcomes are often collected in order to simultaneously assess effectiveness and safety. We develop a Bayesian procedure for determining the required sample size in a regression model where a continuous efficacy variable and a binary safety variable are observed. The sample size determination procedure is simulation based. The model accounts for correlation between the two variables. Through examples we demonstrate that savings in total sample size are possible when the correlation between these two variables is sufficiently high.

Comparison of Bayesian and frequentist meta-analytical approaches for analyzing time to event data.

Using meta-analysis in health care research is a common practice. Here we are interested in methods used for analysis of time-to-event data. Particularly, we are interested in their performance when there is a low event rate. We consider three methods based on the Cox proportional hazards model, including a Bayesian approach. A formal comparison of the methods is conducted using a simulation study. In our simulation we model two treatments and consider several scenarios.

Mobile colloid generation induced by a cementitious plume: mineral surface-charge controls on mobilization.

Cementitious materials are increasingly used as engineered barriers and waste forms for radiological waste disposal. Yet their potential effect on mobile colloid generation is not well-known, especially as it may influence colloid-facilitated contaminant transport. Whereas previous papers have studied the introduction of cement colloids into sediments, this study examined the influence of cement leachate chemistry on the mobilization of colloids from a subsurface sediment collected from the Savannah River Site, USA. A sharp mobile colloid plume formed with the introduction of a cement leachate simulant. Colloid concentrations decreased to background concentrations even though the aqueous chemical conditions (pH and ionic strength) remained unchanged. Mobile colloids were mainly goethite and to a lesser extent kaolinite. The released colloids had negative surface charges and the mean particle sizes ranged primarily from 200 to 470 nm. Inherent mineralogical electrostatic forces appeared to be the controlling colloid removal mechanism in this system. In the background pH of ~6.0, goethite had a positive surface charge, whereas quartz (the dominant mineral in the immobile sediment) and kaolinite had negative surface charges. Goethite acted as a cementing agent, holding kaolinite and itself onto the quartz surfaces due to the electrostatic attraction. Once the pH of the system was elevated, as in the cementitious high pH plume front, the goethite reversed to a negative charge, along with quartz and kaolinite, then goethite and kaolinite colloids were mobilized and a sharp spike in turbidity was observed. Simulating conditions away from the cementitious source, essentially no colloids were mobilized at 1:1000 dilution of the cement leachate or when the leachate pH was ≤ 8. Extreme alkaline pH environments of cementitious leachate may change mineral surface charges, temporarily promoting the formation of mobile colloids.

Your company's history as a leadership tool.

When the history of an organization comes up, it's usually in connection with an anniversary--just part of the "balloons and fireworks" (as one business leader characterized his company's bicentennial celebration, knowing that the investment of time and money would have little staying power). A fast-changing world leaves little time for nostalgia and irrelevant details--or, worse, strategies for winning the last war. But the authors, business historians at the Winthrop Group, assert that leaders with no patience for history are missing a vital truth: A sophisticated understanding of the past is one of the most powerful tools they have for shaping the future. The job of leaders, most would agree, is to inspire collective efforts and devise smart strategies for the future. History can be profitably employed on both fronts. As a leader strives to get people working together productively, communicating the history of the enterprise can instill a sense of identity and purpose and suggest the goals that will resonate. In its most familiar form, as a narrative about the past, history is a rich explanatory tool with which executives can make a case for change and motivate people to overcome challenges. Taken to a higher level, it also serves as a potent problem-solving tool, one that offers pragmatic insights, valid generalizations, and meaningful perspectives--a way to cut through management fads and the noise of the moment to what really matters.