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.

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.

Topical Anti-TNFα Agent Licaminlimab (OCS-02) Relieves Persistent Ocular Discomfort in Severe Dry Eye Disease: A Randomized Phase II Study.

Purpose: To assess the efficacy, safety, and pharmacokinetics of new topical ocular anti-TNFα antibody fragment licaminlimab in the relief of persistent ocular discomfort in severe dry eye disease (DED). Patients and Methods: Patients with ≥6-month history of DED, regular use of artificial tears, and best-corrected visual acuity (BCVA) of ≥55 letters in each eye (Early Treatment Diabetic Retinopathy Score) at baseline were included in this multicenter, randomized, vehicle-controlled, double masked study. A total of 514 patients were screened. After a 2-week run-in with Vehicle, all qualifying patients received Vehicle eye drops for 4 weeks. Patients with global ocular discomfort score ≥50 at the end of this 4-week period were randomized to receive licaminlimab (60 mg/mL ophthalmic solution) (69 patients) or Vehicle (65 patients) for 6 weeks. The primary efficacy endpoint was change from baseline in global ocular discomfort score at Day 29. Safety assessments included adverse events and ophthalmology examination including intraocular pressure (IOP). Serum licaminlimab levels were also determined. Results: Change from baseline to Day 29 in global ocular discomfort score was statistically significantly greater for licaminlimab than for Vehicle (p = 0.041). No safety issues were identified. Serum licaminlimab was undetectable in most patients; the maximum concentration observed was 8.47 ng/mL. Conclusion: Topical ocular licaminlimab demonstrated statistically significant improvement in global ocular discomfort score compared to Vehicle in patients with severe DED, with good tolerability, no increase in IOP, and minimal systemic drug exposure.

Topical Ocular Anti-TNFα Agent Licaminlimab in the Treatment of Acute Anterior Uveitis: A Randomized Phase II Pilot Study.

Purpose: Licaminlimab is a new anti-TNFα antibody fragment for topical ocular application. This phase II study assessed the tolerability, treatment effect, and pharmacokinetics of licaminlimab in acute anterior uveitis (AAU). Methods: In this multicenter, randomized, parallel-group, double-masked study, 43 adult patients with non-infectious AAU and Standardization of Uveitis Nomenclature (SUN) anterior chamber (AC) cell score of 2+ or 3+ were randomized (3:1 ratio) to licaminlimab (60 mg/mL, 8 drops/day for 15 days, 4 drops/day for 7 days, then matching vehicle for 7 days) or dexamethasone eye drops (8 drops/day for 15 days, tapering to 1 drop/day over 14 days). The primary efficacy end point was clinical response (≥2-step decrease in AC cell grade at day 15). A treatment effect was considered as established if the lower limit of the 95% posterior interval of the responder rate was >30%. Serum levels of licaminlimab were determined. Results: The day 15 response rate for licaminlimab was 56%; the lower bound of the 95% credible interval was 40% (i.e. >30%), demonstrating a treatment effect according to prespecified criteria. By day 4, 36% of licaminlimab-treated patients were responders; 76% had an AC cell grade of 0 on ≥1 post-treatment visit. The day 15 dexamethasone response rate was 90% (no inferential between-arm comparison was planned). Both treatments were well-tolerated. Intraocular pressure increased from baseline with dexamethasone but not licaminlimab. Licaminlimab was undetectable in serum in most patients. Conclusions: Licaminlimab is the first biologic demonstrated to have a treatment effect on an intraocular condition with topical ocular application. The trial met its primary objective and the observed responder rate for licaminlimab was 56.0%. Ocular administration of licaminlimab was well-tolerated in adult subjects with AAU for up to 35 days.

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.

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.

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 Approach to Determination of F, D, and Z Values Used in Steam Sterilization Validation.

For manufacturers of sterile drug products, steam sterilization is a common method used to provide assurance of the sterility of manufacturing equipment and products. The validation of sterilization processes is a regulatory requirement and relies upon the estimation of key resistance parameters of microorganisms. Traditional methods have relied upon point estimates for the resistance parameters. In this paper, we propose a Bayesian method for estimation of the well-known DT , z, and Fo values that are used in the development and validation of sterilization processes. A Bayesian approach allows the uncertainty about these values to be modeled using probability distributions, thereby providing a fully risk-based approach to measures of sterility assurance. An example is given using the survivor curve and fraction negative methods for estimation of resistance parameters, and we present a means by which a probabilistic conclusion can be made regarding the ability of a process to achieve a specified sterility criterion.LAY ABSTRACT: For manufacturers of sterile drug products, steam sterilization is a common method used to provide assurance of the sterility of manufacturing equipment and products. The validation of sterilization processes is a regulatory requirement and relies upon the estimation of key resistance parameters of microorganisms. Traditional methods have relied upon point estimates for the resistance parameters. In this paper, we propose a Bayesian method for estimation of the critical process parameters that are evaluated in the development and validation of sterilization processes. A Bayesian approach allows the uncertainty about these parameters to be modeled using probability distributions, thereby providing a fully risk-based approach to measures of sterility assurance. An example is given using the survivor curve and fraction negative methods for estimation of resistance parameters, and we present a means by which a probabilistic conclusion can be made regarding the ability of a process to achieve a specified sterility criterion.

"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.

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.

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.

Earliest archaeological evidence of persistent hominin carnivory.

The emergence of lithic technology by ≈ 2.6 million years ago (Ma) is often interpreted as a correlate of increasingly recurrent hominin acquisition and consumption of animal remains. Associated faunal evidence, however, is poorly preserved prior to ≈ 1.8 Ma, limiting our understanding of early archaeological (Oldowan) hominin carnivory. Here, we detail three large well-preserved zooarchaeological assemblages from Kanjera South, Kenya. The assemblages date to 2.0 Ma, pre-dating all previously published archaeofaunas of appreciable size. At Kanjera, there is clear evidence that Oldowan hominins acquired and processed numerous, relatively complete, small ungulate carcasses. Moreover, they had at least occasional access to the fleshed remains of larger, wildebeest-sized animals. The overall record of hominin activities is consistent through the stratified sequence - spanning hundreds to thousands of years - and provides the earliest archaeological evidence of sustained hominin involvement with fleshed animal remains (i.e., persistent carnivory), a foraging adaptation central to many models of hominin evolution.

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.

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.

Performance and sample size requirements of Bayesian methods for binary outcomes in fixed-dose combination drug studies.

We develop a Bayesian analysis for the study of fixed-dose combinations of two or more drugs. The approach described here does not require knowledge of the dose-response relationships of the components or large sample approximations. We provide a procedure to estimate sample size in this context. In addition, we explore the performance of the Bayesian procedure in situations where existing methods are known to perform poorly.

Binary regression with misclassified response and covariate subject to measurement error: a bayesian approach.

We consider a Bayesian analysis for modeling a binary response that is subject to misclassification. Additionally, an explanatory variable is assumed to be unobservable, but measurements are available on its surrogate. A binary regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in the response. Unlike existing methods, no model parameters need be assumed known. Markov chain Monte Carlo methods are utilized to perform the necessary computations. The methods developed are illustrated using atomic bomb survival data. A simulation experiment explores advantages of the approach.

Prediction of healing for postoperative diabetic foot wounds based on early wound area progression.

OBJECTIVE: To evaluate the probability of wound healing based on percentage of wound area reduction (PWAR) at 1 and 4 weeks in individuals with large, chronic, nonischemic diabetic foot wounds following partial foot amputation. METHODS: Data from a 16-week randomized clinical trial (RCT) of 162 patients were analyzed to compare outcomes associated with negative-pressure wound therapy (NPWT) delivered through the V.A.C. Therapy System (Kinetic Concepts, San Antonio, TX) (n = 77) versus standard moist wound therapy (MWT) (n = 85). The 1- and 4-week regression models included 153 and 129 of the RCT patients, respectively. RESULTS: Early changes in PWAR were predictive of final healing at 16 weeks. Specifically, wounds that reached >or=15% PWAR at 1 week or >or=60% PWAR at 4 weeks had a 68 and 77% (respectively) probability of healing vs. a 31 and 30% probability if these wound area reductions were not achieved. Patients receiving NPWT were 2.5 times more likely to achieve both a 15% PWAR at 1 week and a 60% area reduction at 1 month (odds ratios 2.51 and 2.49, respectively) compared with those receiving MWT. CONCLUSION: Results of this study suggest that clinicians can calculate the PWAR of a wound as early as 1 week into treatment to predict the likelihood of healing at 16 weeks. This might also assist in identifying a rationale to reevaluate the wound and change wound therapies.

A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression.

Response misclassification of counted data biases and understates the uncertainty of parameter estimators in Poisson regression models. To correct these problems, researchers have devised classical procedures that rely on asymptotic distribution results and supplemental validation data in order to estimate unknown misclassification parameters. We derive a new Bayesian Poisson regression procedure that accounts and corrects for misclassification for a count variable with two categories. Under the Bayesian paradigm, one can use validation data, expert opinion, or a combination of these two approaches to correct for the consequences of misclassification. The Bayesian procedure proposed here yields an operationally effective way to correct and account for misclassification effects in Poisson count regression models. We demonstrate the performance of the model in a simulation study. Additionally, we analyze two real-data examples and compare our new Bayesian inference method that adjusts for misclassification with a similar analysis that ignores misclassification.