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Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improving 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 manuscript, 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.

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 Matern 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.

Estimating causal effects from randomized controlled trials is often complicated due to participant noncompliance to randomized treatment. Although there are a variety of methods to estimate causal effects in the presence of noncompliance, they generally make the assumption that noncompliance is measured without error. This is frequently an untenable assumption, particularly when noncompliance is based on participant self-report. To overcome this issue, we treat compliance as an unobserved variable and show how to estimate the probability of compliance given a biomarker of treatment and the other observed data. We present inverse probability weighted estimators, regression-based estimators, and a doubly-robust augmented estimator that rely on the estimated probability of compliance rather than an indicator of compliance. We investigate the finite-sample properties of the estimators and their efficiency and robustness under correctly specified or misspecified models, and we apply the estimators to a recently completed trial of very low nicotine content cigarettes.

Multiparametric magnetic resonance imaging (mpMRI), which combines traditional anatomic and newer quantitative MRI methods, has been shown to result in improved voxel-wise classification of prostate cancer as compared with any single MRI parameter. While these results are promising, substantial heterogeneity in the mpMRI parameter values and voxel-wise prostate cancer risk has been observed both between and within regions of the prostate. This suggests that classification of prostate cancer can potentially be improved by incorporating structural information into the classifier. In this paper, we propose a novel voxel-wise classifier of prostate cancer that accounts for the anatomic structure of the prostate by Bayesian hierarchical modeling, which can be combined with post hoc spatial Gaussian kernel smoothing to account for residual spatial correlation. Our proposed classifier results in significantly improved area under the ROC curve (0.822 vs 0.729, P < .001) and sensitivity corresponding to 90% specificity (0.599 vs 0.429, P < .001), compared with a baseline model that does not account for the anatomic structure of the prostate. Furthermore, the classifier can also be applied on voxels with missing mpMRI parameters, resulting in similar performance, which is an important practical consideration that cannot be easily accommodated using regression-based classifiers. In addition, our classifier achieved high computational efficiency with a closed-form solution for the posterior predictive cancer probability.

Phase I-II clinical trials refer to the class of designs that evaluate both the safety and efficacy of a novel therapeutic agent in a single trial. Typically, Phase I-II oncology trials take the form of dose-escalation studies, where initial subjects are treated at the lowest dose level and subsequent subjects are treated at progressively higher doses until the optimal dose is identified. While dose-escalation designs are well-motivated in the case of traditional chemotherapeutic agents, an alternate approach may be considered for therapeutic cancer vaccines, where an investigator's main objective is to evaluate the safety and efficacy of a set of dosing schedules or adjuvant combinations rather than to compare the safety and efficacy of progressively higher dose levels. We present a two-stage, Bayesian adaptive Phase I-II trial design to evaluate the safety and efficacy of therapeutic cancer vaccines. In the first stage, we determine whether a vaccination schedule achieves a minimum level of performance by comparing the toxicity and immune response rates to historical benchmarks. Vaccination schedules that achieve a minimum level of performance are compared using their magnitudes of immune response. If the superiority of a single schedule cannot be established after the first stage, Bayesian posterior predictive probabilities are used to determine the additional sample size required to identify the optimal vaccination schedule in a second stage. Copyright © 2016 John Wiley & Sons, Ltd.

Group sequential testing procedures have been proposed as an approach to conserving resources in biomarker validation studies. Previously, Koopmeiners and Feng (2011) derived the asymptotic properties of the sequential empirical positive predictive value (PPV) and negative predictive value curves, which summarize the predictive accuracy of a continuous marker, under case-control sampling. A limitation of their approach is that the prevalence can not be estimated from a case-control study and must be assumed known. In this manuscript, we consider group sequential testing of the predictive accuracy of a continuous biomarker with unknown prevalence. First, we develop asymptotic theory for the sequential empirical PPV and NPV curves when the prevalence must be estimated, rather than assumed known in a case-control study. We then discuss how our results can be combined with standard group sequential methods to develop group sequential testing procedures and bias-adjusted estimators for the PPV and NPV curve. The small sample properties of the proposed group sequential testing procedures and estimators are evaluated by simulation and we illustrate our approach in the context of a study to validate a novel biomarker for prostate cancer.

Multi-parametric magnetic resonance imaging (mpMRI), which combines traditional anatomic and newer quantitative MRI methods, has been shown to result in improved voxel-wise classification of prostate cancer as compared to any single MRI parameter. While these results are promising, substantial heterogeneity in the mpMRI parameter values and voxel-wise prostate cancer risk has been observed both between and within regions of the prostate. This suggests that classification of prostate cancer can potentially be improved by incorporating structural information into the classifier. In this paper, we propose a novel voxel-wise classifier of prostate cancer that accounts for the anatomic structure of the prostate by Bayesian hierarchical modeling, which can be combined with post-hoc spatial Gaussian kernel smoothing to account for residual spatial correlation. Our proposed classifier results in significantly improved area under the ROC curve (0.822 vs. 0.729, p < 0.001) and sensitivity corresponding to 90% specificity (0.599 vs. 0.429, p < 0.001), compared to a baseline model that does not account for the anatomic structure of the prostate. Furthermore, the classifier can also be applied on voxels with missing mpMRI parameters, resulting in similar performance, which is an important practical consideration that cannot be easily accommodated using regression-based classifiers. In addition, our classifier achieved high computational efficiency with a closed-form solution for the posterior predictive cancer probability.

Two-stage designs to develop and validate a panel of biomarkers present a natural setting for the inclusion of stopping rules for futility in the event of poor preliminary estimates of performance. We consider the design of a two-stage study to develop and validate a panel of biomarkers where a predictive model is developed using a subset of the samples in stage 1 and the model is validated using the remainder of the samples in stage 2. First, we illustrate how we can implement a stopping rule for futility in a standard, two-stage study for developing and validating a predictive model where samples are separated into a training sample and a validation sample. Simulation results indicate that our design has type I error rate and power similar to the fixed-sample design but with a substantially reduced sample size under the null hypothesis. We then illustrate how we can include additional interim analyses in stage 2 by applying existing group sequential methodology, which results in even greater savings in the number of samples required under both the null and the alternative hypotheses. Our simulation results also illustrate that the operating characteristics of our design are robust to changes in the underlying marker distribution.

Recently, many Bayesian methods have been developed for dose finding when simultaneously modeling both toxicity and efficacy outcomes in a blended phase I/II fashion. A further challenge arises when all the true efficacy data cannot be obtained quickly after the treatment so that surrogate markers are instead used (e.g., in cancer trials). We propose a framework to jointly model the probabilities of toxicity, efficacy, and surrogate efficacy given a particular dose. Our trivariate binary model is specified as a composition of two bivariate binary submodels. In particular, we extend the bivariate continual reassessment method (CRM), as well as utilize a particular Gumbel copula. The resulting trivariate algorithm utilizes all the available data at any given time point and can flexibly stop the trial early for either toxicity or efficacy. Our simulation studies demonstrate that our proposed method can successfully improve dosage targeting efficiency and guard against excess toxicity over a variety of true model settings and degrees of surrogacy. Copyright © 2012 John Wiley & Sons, Ltd.

Development of a disease screening biomarker involves several phases. In phase 2 its sensitivity and specificity is compared with established thresholds for minimally acceptable performance. Since we anticipate that most candidate markers will not prove to be useful and availability of specimens and funding is limited, early termination of a study is appropriate, if accumulating data indicate that the marker is inadequate. Yet, for markers that complete phase 2, we seek estimates of sensitivity and specificity to proceed with the design of subsequent phase 3 studies. We suggest early stopping criteria and estimation procedures that adjust for bias caused by the early termination option. An important aspect of our approach is to focus on properties of estimates conditional on reaching full study enrollment. We propose the conditional-UMVUE and contrast it with other estimates, including naive estimators, the well-studied unconditional-UMVUE and the mean and median Whitehead-adjusted estimators. The conditional-UMVUE appears to be a very good choice.

Phase I-II clinical trials refer to the class of designs that evaluate both the safety and efficacy of a novel therapeutic agent in a single trial. Typically, Phase I-II oncology trials take the form of dose-escalation studies, where initial subjects are treated at the lowest dose level and subsequent subjects are treated at progressively higher doses until the optimal dose is identified. While dose-escalation designs are well-motivated in the case of traditional chemotherapeutic agents, an alternate approach may be considered for therapeutic cancer vaccines, where an investigator's main objective is to evaluate the safety and efficacy of a set of dosing schedules or adjuvant combinations rather than to compare the safety and efficacy of progressively higher dose levels. We present a two-stage, Bayesian adaptive Phase I-II trial design to evaluate the safety and efficacy of therapeutic cancer vaccines. In the first stage, we determine whether a vaccination schedule achieves a minimum level of performance by comparing the toxicity and immune response rates to historical benchmarks. Vaccination schedules that achieve a minimum level of performance are compared using their magnitudes of immune response. If the superiority of a single schedule cannot be established after the first stage, Bayesian posterior predictive probabilities are used to determine the additional sample size required to identify the optimal vaccination schedule in a second stage. Copyright © 2016 John WileySons, Ltd.