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Mechanistic models are key to providing reliable information for developing infectious disease control strategies. In general, these models are fitted in Bayesian Markov chain Monte Carlo (MCMC) frameworks that incorporate heterogeneities within a population. However, these frameworks have the major drawback of being computationally expensive. This problem is even more severe when the epidemic history is incomplete, such as unknown infection times. Instead of using the time-consuming Bayesian MCMC methods, this paper explores the use of supervised classification methods to analyze the infectious disease data incorporating infection time uncertainty. The epidemic generating models are classified based on summary statistics of epidemics as inputs. The validity of these methods is investigated by using simulated epidemic data and Tomato Spotted Wilt Virus (TSWV) data, accounting for unknown infectious periods and infection times of individuals. We show that these methods are capable of capturing biological characteristics of disease transmission dynamics when there is infection time uncertainty in infectious disease data. [ABSTRACT FROM AUTHOR]

When making decisions about whether a product can meet required performance standards, it is often of interest to make decisions as soon as enough information has been obtained–even if that is before the conclusion of the planned test or experimental design. Evaluating data with the intent of stopping a test early is referred to as interim analysis, which can be used to address time and cost constraints. In such cases, it is important to execute tests as efficiently and effectively as possible. A Bayesian method for improving efficiency is to use informative priors to incorporate previous information—such as expert opinion or previous data—to create more precise parameter estimates and avoid allocating costly resources in a sub-optimal manner. We consider the class of power priors and variants thereof, proposing a variant that allows for a computationally efficient MCMC sampling method in an interim analysis setting to evaluate a product. A novel power prior that accounts for differences and similarities between current and previous data sets, and its role in analysis, is considered in an interim analysis construct. Simulations demonstrate previous information can be leveraged using this proposed prior to stop testing early and obtain more precise parameter estimates. [ABSTRACT FROM AUTHOR]

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment. [ABSTRACT FROM AUTHOR]

ABSTRACT: Heavy selection for growth in turkeys has led to a decay in leg soundness and walking ability. In this study, different models and traits were used to investigate the genetic relationships between body weight (BW) and walking ability (WA) in a turkey population. The data consisted of BW and WA traits collected on 276,059 male birds. Body weight was measured at 12 and 20 wk and WA at 20 wk of age. For WA, birds were scored based on a 1 (bad) to 6 (good) grading system. Due to the small number of records with scores 5 and 6, birds with WA scores of 4, 5, and 6 were grouped together resulting in only 4 classes. Additionally, a binary classification of WA (scores 1 and 2 = Similarly, an estimate of the genetic correlation between WA and BW at 20 wk was −0.45, indicating a more pronounced class 1; scores 3, 4, 5, and 6 = class 2) was evaluated. The inheritability estimates of WA ranged between 0.25 and 0.27 depending on the number of classes. The Heritability of BW at 12 and 20 wk was 0.44 and 0.51, respectively. The genetic correlation between WA and BW at 12 wk was around −0.35, indicating that heavy birds tend to have poor WA. antagonistic relationship between BW and WA. The genetic correlation between BW at 12 and 20 wk was positive and high (0.80). The residual correlation between WA and BW at 12 and 20 wk of age was −0.07 and −0.02, respectively. The residual correlation between body weight traits was 0.57. Similar results were observed when a binary classification was adopted for WA. The probability of an individual with a given genetic merit expressing a certain class of WA was determined for different fixed effect designations. Predictive probabilities clearly showed that birds when hatched in the winter would have a small chance to exhibit good WA phenotypes.

Abstract Background Low birth weight is a public health problem in Africa with the cause attributable to malaria in pregnancy. World Health Organization recommends the use of intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine to prevent malaria during pregnancy. The objective of this study was to evaluate the prevalence and trajectories of birth weight and the direct impact and relationship between sulfadoxine-pyrimethamine and birth weight in Ghana since 2003. Method This study used secondary data obtained from the Demographic and Health Survey conducted in Ghana since 2003. Low birth weight was defined as weight 4000 g. In all the analysis, we adjusted for clustering, stratification and weighting to reduce bias and improve precision of the estimates. Analysis was performed on each survey year as well as the pooled dataset. The generalized ordered partial proportional odds model was used due to violations of the parallel regression model assumptions. Efforts were made to identify all confounding variables and these were adjusted for. Predictive analysis was also executed. Results The overall prevalence of low birth weight was 9% while that of macrosomia was 13%. The low birth weight for 2003 was 12% while in 2008 it was 21% and then 68% in 2014. The mean birth weight of the children in 2014 was 3.16 (3.14, 3.19), 2008 was 3.37 (3.28, 3.45) and 2003 was 3.59 (3.49, 3.69) while that of the pooled data was 3.28 (3.25, 3.30). The adjusted model (taking into consideration all confounding variables) showed that non-uptake of SP could result in 51% odds of giving birth to a low-birth-weight compared with normal birth weight child. An insignificant result was observed between macrosomia and low birth weight. Conclusion There is higher probability that low birth weight could increase over the next couple of years if measures are not taking to reverse the current trajectories. The uptake of sulfadoxine-pyrimethamine should continue to be encouraged and recommended because it has a direct beneficial effect on the weight of the child.

IntroductionEfforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. With efforts to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of evidence acquired in single-arm phase II clinical trials. And yet, in the absence of randomization, patient prognosis for progression-free survival and overall survival may not have been studied under standard of care chemotherapies for emerging biomarker subpopulations prior to the submission of an accelerated approval application. Historical control rates used to design and evaluate emerging targeted therapies often arise as population averages, lacking specificity to the targeted genetic or immunophenotypic profile. Thus, historical trial results are inherently limited for inferring the potential “comparative efficacy” of novel targeted therapies. Consequently, randomization may be unavoidable in this setting. Innovations in design methodology are needed, however, to enable efficient implementation of randomized trials for agents that target biomarker subpopulations.MethodsThis article proposes three randomized designs for early phase biomarker-guided oncology clinical trials. Each design utilizes the optimal efficiency predictive probability method to monitor multiple biomarker subpopulations for futility. Only designs with type I error between 0.05 and 0.1 and power of at least 0.8 were considered when selecting an optimal efficiency design from among the candidate designs formed by different combinations of posterior and predictive threshold. A simulation study motivated by the results reported in a recent clinical trial studying atezolizumab treatment in patients with locally advanced or metastatic urothelial carcinoma is used to evaluate the operating characteristics of the various designs.ResultsOut of a maximum of 300 total patients, we find that the enrichment design has an average total sample size under the null of 101.0 and a total average sample size under the alternative of 218.0, as compared to 144.8 and 213.8 under the null and alternative, respectively, for the stratified control arm design. The pooled control arm design enrolled a total of 113.2 patients under the null and 159.6 under the alternative, out of a maximum of 200. These average sample sizes that are 23-48% smaller under the alternative and 47-64% smaller under the null, as compared to the realized sample size of 310 patients in the phase II study of atezolizumab.DiscussionOur findings suggest that potentially smaller phase II trials to those used in practice can be designed using randomization and futility stopping to efficiently obtain more information about both the treatment and control groups prior to phase III study.

In this short note, we express our viewpoint regarding declaring study success based on Bayesian predictive probability of study success. [ABSTRACT FROM AUTHOR]

Using a dataset of 3108 dichotomous renminbi (RMB) misalignment estimates from 95 studies, we conduct a meta‐analysis based on Bayesian model averaging (BMA) to assess the effects of study characteristics on these RMB misalignment estimates. The findings demonstrate that selected study characteristic types systematically influence RMB misalignment estimates, especially time series versus non‐time series approach and co‐integration versus non‐cointegration. Though lean toward RMB undervaluation inference, results from the BMA meta‐analysis and the corresponding median probability model and synthetic practice evaluation do not offer a conclusive statistical inference on RMB misalignment. Specifically, the evidence varies with the configuration of model specification and does not corroborate the RMB undervaluation claim for the periods of "2009–2010" and "2011–2014." [ABSTRACT FROM AUTHOR]

In oncology, Phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent Phase III trials. Traditionally, Phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a Phase III study at the scheduled end of the trial. Therefore, we propose a new simple index vector to summarize the results that cannot be captured by existing strategies. Specifically, we define the worst and most promising situations for the potential effect of a treatment, then use the proposed index vector to measure the deviation between the two situations. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions., (© 2024 John Wiley & Sons Ltd.)

In phase II oncology trials, two‐stage design allowing early stopping for futility and/or efficacy is frequently used. However, this design based on frequentist statistical approaches could not guarantee a high posterior probability of attending the pre‐specified clinically interesting rate from a Bayesian perspective. Here, we proposed a new Bayesian design enabling early terminating for efficacy as well as futility. In addition to the clinically uninteresting and interesting response rate, a prior distribution of response rate, the minimum posterior threshold probabilities and the lengths of the highest posterior density intervals were specified in the design. Finally, we defined the feasible design with the highest total effective predictive probability. We studied the properties of the proposed design and applied it to an oncology trial as an example. The proposed design ensured that the observed response rate fell within prespecified levels of posterior probability. The proposed design provides an alternative design to single‐arm two‐stage trials. [ABSTRACT FROM AUTHOR]

Background: Low birth weight is a public health problem in Africa with the cause attributable to malaria in pregnancy. World Health Organization recommends the use of intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine to prevent malaria during pregnancy. The objective of this study was to evaluate the prevalence and trajectories of birth weight and the direct impact and relationship between sulfadoxine-pyrimethamine and birth weight in Ghana since 2003.Method: This study used secondary data obtained from the Demographic and Health Survey conducted in Ghana since 2003. Low birth weight was defined as weight < 2500 g irrespective of the gestational age of the foetus, while normal birth weight was between 2500 g to < 4000 g and macrosomia was =  > 4000 g. In all the analysis, we adjusted for clustering, stratification and weighting to reduce bias and improve precision of the estimates. Analysis was performed on each survey year as well as the pooled dataset. The generalized ordered partial proportional odds model was used due to violations of the parallel regression model assumptions. Efforts were made to identify all confounding variables and these were adjusted for. Predictive analysis was also executed.Results: The overall prevalence of low birth weight was 9% while that of macrosomia was 13%. The low birth weight for 2003 was 12% while in 2008 it was 21% and then 68% in 2014. The mean birth weight of the children in 2014 was 3.16 (3.14, 3.19), 2008 was 3.37 (3.28, 3.45) and 2003 was 3.59 (3.49, 3.69) while that of the pooled data was 3.28 (3.25, 3.30). The adjusted model (taking into consideration all confounding variables) showed that non-uptake of SP could result in 51% odds of giving birth to a low-birth-weight compared with normal birth weight child. An insignificant result was observed between macrosomia and low birth weight.Conclusion: There is higher probability that low birth weight could increase over the next couple of years if measures are not taking to reverse the current trajectories. The uptake of sulfadoxine-pyrimethamine should continue to be encouraged and recommended because it has a direct beneficial effect on the weight of the child. [ABSTRACT FROM AUTHOR]

In this paper, a collision avoidance algorithm based on predictive probability using the Kalman filter is proposed. Existing algorithms are based on the Time to the Closest Point of Approach (TCPA) and Distance to the Closest Point of Approach (DCPA) derived from geometric data between own ship and target ships using current states (heading angle and velocity). When compared to these approaches, the proposed algorithm is capable of predicting the states and easy to plan optimal path by considering the uncertainty in avoidance situation based on the number of ships. In this study, an Unscented Kalman Filter (UKF) is used due to the nonlinearities in the state variables to be predicted. By using the predicted state variables of the ships calculated via the UKF's prediction step, the positions of the number of ships after a certain time are derived as a predictive probability. Then, the validation of the proposed algorithm is examined by planning the optimal path in the collision avoidance simulations by applying the predictive probability.

Most existing phase II clinical trial designs focus on conventional chemotherapy with binary tumor response as the endpoint. The advent of novel therapies, such as molecularly targeted agents and immunotherapy, has made the endpoint of phase II trials more complicated, often involving ordinal, nested, and coprimary endpoints. We propose a simple and flexible Bayesian optimal phase II predictive probability (OPP) design that handles binary and complex endpoints in a unified way. The Dirichlet‐multinomial model is employed to accommodate different types of endpoints. At each interim, given the observed interim data, we calculate the Bayesian predictive probability of success, should the trial continue to the maximum planned sample size, and use it to make the go/no‐go decision. The OPP design controls the type I error rate, maximizes power or minimizes the expected sample size, and is easy to implement, because the go/no‐go decision boundaries can be enumerated and included in the protocol before the onset of the trial. Simulation studies show that the OPP design has satisfactory operating characteristics. [ABSTRACT FROM AUTHOR]

Abstract Background The common frequentist approach is limited in providing investigators with appropriate measures for conducting a new trial. To answer such important questions and one has to look at Bayesian statistics. Methods As a worked example, we conducted a Bayesian cumulative meta-analysis to summarize the benefit of patient-specific instrumentation on the alignment of total knee replacement from previously published evidence. Data were sourced from Medline, Embase, and Cochrane databases. All randomised controlled comparisons of the effect of patient-specific instrumentation on the coronal alignment of total knee replacement were included. The main outcome was the risk difference measured by the proportion of failures in the control group minus the proportion of failures in the experimental group. Through Bayesian statistics, we estimated cumulatively over publication time of the trial results: the posterior probabilities that the risk difference was more than 5 and 10%; the posterior probabilities that given the results of all previous published trials an additional fictive trial would achieve a risk difference of at least 5%; and the predictive probabilities that observed failure rate differ from 5% across arms. Results Thirteen trials were identified including 1092 patients, 554 in the experimental group and 538 in the control group. The cumulative mean risk difference was 0.5% (95% CrI: −5.7%; +4.5%). The posterior probabilities that the risk difference be superior to 5 and 10% was less than 5% after trial #4 and trial #2 respectively. The predictive probability that the difference in failure rates was at least 5% dropped from 45% after the first trial down to 11% after the 13th. Last, only unrealistic trial design parameters could change the overall evidence accumulated to date. Conclusions Bayesian probabilities are readily understandable when discussing the relevance of performing a new trial. It provides investigators the current probability that an experimental treatment be superior to a reference treatment. In case a trial is designed, it also provides the predictive probability that this new trial will reach the targeted risk difference in failure rates. Trial registration CRD42015024176 .

The evolution of "informatics" technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high‐dimensionality of omics‐type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods. [ABSTRACT FROM AUTHOR]

Heavy selection for growth in turkeys has led to a decay in leg soundness and walking ability. In this study, different models and traits were used to investigate the genetic relationships between body weight (BW) and walking ability (WA) in a turkey population. The data consisted of BW and WA traits collected on 276,059 male birds. Body weight was measured at 12 and 20 wk and WA at 20 wk of age. For WA, birds were scored based on a 1 (bad) to 6 (good) grading system. Due to the small number of records with scores 5 and 6, birds with WA scores of 4, 5, and 6 were grouped together resulting in only 4 classes. Additionally, a binary classification of WA (scores 1 and 2 = Similarly, an estimate of the genetic correlation between WA and BW at 20 wk was −0.45, indicating a more pronounced class 1; scores 3, 4, 5, and 6 = class 2) was evaluated. The inheritability estimates of WA ranged between 0.25 and 0.27 depending on the number of classes. The Heritability of BW at 12 and 20 wk was 0.44 and 0.51, respectively. The genetic correlation between WA and BW at 12 wk was around −0.35, indicating that heavy birds tend to have poor WA. antagonistic relationship between BW and WA. The genetic correlation between BW at 12 and 20 wk was positive and high (0.80). The residual correlation between WA and BW at 12 and 20 wk of age was −0.07 and −0.02, respectively. The residual correlation between body weight traits was 0.57. Similar results were observed when a binary classification was adopted for WA. The probability of an individual with a given genetic merit expressing a certain class of WA was determined for different fixed effect designations. Predictive probabilities clearly showed that birds when hatched in the winter would have a small chance to exhibit good WA phenotypes. [ABSTRACT FROM AUTHOR]

In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials. [ABSTRACT FROM AUTHOR]

Molecular targeted therapies come often with lower toxicity profiles than traditional cytotoxic treatments, thus shifting drug development paradigm into establishing evidence of biological activity, target modulation, and pharmacodynamics effects of these therapies in early phase trials. Therefore, these trials need to address simultaneous evaluation of safety, proof-of-concept biological marker activity, or changes in continuous tumor size instead of binary response rate. Interim analyses are typically incorporated in the trial due to concerns regarding excessive toxicity and ineffective new treatment. There is a lack of interim strategies developed to monitor futility and/or efficacy for these types of continuous outcomes, especially in single-arm phase II trials. We propose a 2-stage design based on predictive probability to accommodate continuous endpoints, assuming a normal distribution with known variance. Simulation results and case study demonstrated that the proposed design can incorporate an interim stop for futility as well as for efficacy while maintaining desirable design properties. As expected, using continuous tumor size resulted in reduced sample sizes for both optimal and minimax designs. A limited exploration of various priors was performed and shown to be robust. As research rapidly moves to incorporate more molecular targeted therapies, it will accommodate new types of outcomes while allowing for flexible stopping rules to continue optimizing trial resources and prioritize agents with compelling early phase data. [ABSTRACT FROM AUTHOR]

The process of screening agents one-at-a-time under the current clinical trials system suffers from several deficiencies that could be addressed in order to extend financial and patient resources. In this article, we introduce a statistical framework for designing and conducting randomized multi-arm screening platforms with binary endpoints using Bayesian modeling. In essence, the proposed platform design consolidates inter-study control arms, enables investigators to assign more new patients to novel therapies, and accommodates mid-trial modifications to the study arms that allow both dropping poorly performing agents as well as incorporating new candidate agents. When compared to sequentially conducted randomized two-arm trials, screening platform designs have the potential to yield considerable reductions in cost, alleviate the bottleneck between phase I and II, eliminate bias stemming from inter-trial heterogeneity, and control for multiplicity over a sequence of a priori planned studies. When screening five experimental agents, our results suggest that platform designs have the potential to reduce the mean total sample size by as much as 40% and boost the mean overall response rate by as much as 15%. We explain how to design and conduct platform designs to achieve the aforementioned aims and preserve desirable frequentist properties for the treatment comparisons. In addition, we demonstrate how to conduct a platform design using look-up tables that can be generated in advance of the study. The gains in efficiency facilitated by platform design could prove to be consequential in oncologic settings, wherein trials often lack a proper control, and drug development suffers from low enrollment, long inter-trial latency periods, and an unacceptably high rate of failure in phase III. [ABSTRACT FROM AUTHOR]

Background: The common frequentist approach is limited in providing investigators with appropriate measures for conducting a new trial. To answer such important questions and one has to look at Bayesian statistics. Methods: As a worked example, we conducted a Bayesian cumulative meta-analysis to summarize the benefit of patient-specific instrumentation on the alignment of total knee replacement from previously published evidence. Data were sourced from Medline, Embase, and Cochrane databases. All randomised controlled comparisons of the effect of patient-specific instrumentation on the coronal alignment of total knee replacement were included. The main outcome was the risk difference measured by the proportion of failures in the control group minus the proportion of failures in the experimental group. Through Bayesian statistics, we estimated cumulatively over publication time of the trial results: the posterior probabilities that the risk difference was more than 5 and 10%; the posterior probabilities that given the results of all previous published trials an additional fictive trial would achieve a risk difference of at least 5%; and the predictive probabilities that observed failure rate differ from 5% across arms. Results: Thirteen trials were identified including 1092 patients, 554 in the experimental group and 538 in the control group. The cumulative mean risk difference was 0.5% (95% CrI: -5.7%; +4.5%). The posterior probabilities that the risk difference be superior to 5 and 10% was less than 5% after trial #4 and trial #2 respectively. The predictive probability that the difference in failure rates was at least 5% dropped from 45% after the first trial down to 11% after the 13th. Last, only unrealistic trial design parameters could change the overall evidence accumulated to date. Conclusions: Bayesian probabilities are readily understandable when discussing the relevance of performing a new trial. It provides investigators the current probability that an experimental treatment be superior to a reference treatment. In case a trial is designed, it also provides the predictive probability that this new trial will reach the targeted risk difference in failure rates. [ABSTRACT FROM AUTHOR]

Background: The common frequentist approach is limited in providing investigators with appropriate measures for conducting a new trial. To answer such important questions and one has to look at Bayesian statistics.Methods: As a worked example, we conducted a Bayesian cumulative meta-analysis to summarize the benefit of patient-specific instrumentation on the alignment of total knee replacement from previously published evidence. Data were sourced from Medline, Embase, and Cochrane databases. All randomised controlled comparisons of the effect of patient-specific instrumentation on the coronal alignment of total knee replacement were included. The main outcome was the risk difference measured by the proportion of failures in the control group minus the proportion of failures in the experimental group. Through Bayesian statistics, we estimated cumulatively over publication time of the trial results: the posterior probabilities that the risk difference was more than 5 and 10%; the posterior probabilities that given the results of all previous published trials an additional fictive trial would achieve a risk difference of at least 5%; and the predictive probabilities that observed failure rate differ from 5% across arms.Results: Thirteen trials were identified including 1092 patients, 554 in the experimental group and 538 in the control group. The cumulative mean risk difference was 0.5% (95% CrI: -5.7%; +4.5%). The posterior probabilities that the risk difference be superior to 5 and 10% was less than 5% after trial #4 and trial #2 respectively. The predictive probability that the difference in failure rates was at least 5% dropped from 45% after the first trial down to 11% after the 13th. Last, only unrealistic trial design parameters could change the overall evidence accumulated to date.Conclusions: Bayesian probabilities are readily understandable when discussing the relevance of performing a new trial. It provides investigators the current probability that an experimental treatment be superior to a reference treatment. In case a trial is designed, it also provides the predictive probability that this new trial will reach the targeted risk difference in failure rates.Trial Registration: CRD42015024176 . [ABSTRACT FROM AUTHOR]

Futility analyses provide a mechanism to stop a trial early because of low likelihood to achieve its efficacy objective. They are usually motivated by ethical and economic purposes, so that stopping a trial with poor efficacy could save patients and resources for other promising trials. There are various methods to address futility analyses in the literature but most focus on equally spaced interim looks. We consider a constrained optimization framework where the timing and the futility boundary are decided jointly to balance the risks between stopping trials which should continue, and continuing trials which should stop. The average sample size is used as a key parameter, which is evaluated under different degrees of power loss. Alternative objective functions and constraints are compared to assess the operating characteristics of the optimal futility scheme. Numerical results for single and multiple futility looks are provided. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

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Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches. [ABSTRACT FROM AUTHOR]

Introduction: Efforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. With efforts to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of evidence acquired in single-arm phase II clinical trials. And yet, in the absence of randomization, patient prognosis for progression-free survival and overall survival may not have been studied under standard of care chemotherapies for emerging biomarker subpopulations prior to the submission of an accelerated approval application. Historical control rates used to design and evaluate emerging targeted therapies often arise as population averages, lacking specificity to the targeted genetic or immunophenotypic profile. Thus, historical trial results are inherently limited for inferring the potential "comparative efficacy" of novel targeted therapies. Consequently, randomization may be unavoidable in this setting. Innovations in design methodology are needed, however, to enable efficient implementation of randomized trials for agents that target biomarker subpopulations., Methods: This article proposes three randomized designs for early phase biomarker-guided oncology clinical trials. Each design utilizes the optimal efficiency predictive probability method to monitor multiple biomarker subpopulations for futility. Only designs with type I error between 0.05 and 0.1 and power of at least 0.8 were considered when selecting an optimal efficiency design from among the candidate designs formed by different combinations of posterior and predictive threshold. A simulation study motivated by the results reported in a recent clinical trial studying atezolizumab treatment in patients with locally advanced or metastatic urothelial carcinoma is used to evaluate the operating characteristics of the various designs., Results: Out of a maximum of 300 total patients, we find that the enrichment design has an average total sample size under the null of 101.0 and a total average sample size under the alternative of 218.0, as compared to 144.8 and 213.8 under the null and alternative, respectively, for the stratified control arm design. The pooled control arm design enrolled a total of 113.2 patients under the null and 159.6 under the alternative, out of a maximum of 200. These average sample sizes that are 23-48% smaller under the alternative and 47-64% smaller under the null, as compared to the realized sample size of 310 patients in the phase II study of atezolizumab., Discussion: Our findings suggest that potentially smaller phase II trials to those used in practice can be designed using randomization and futility stopping to efficiently obtain more information about both the treatment and control groups prior to phase III study., Competing Interests: BH declares advising/consulting for Amgen, Bayer HealthCare Pharmaceuticals Inc., Telperian, and STCube Pharmaceuticals. NP declares advising/consulting for AstraZeneca, Merck, Pfizer, Eli Lilly/LOXO, Genentech, BMS, Amgen, Mirati, Inivata, G1 Therapeutics, Viosera, Xencor, Janssen, Boehringer Ingelheim, and Sanofi-Genzyme. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zabor, Kaizer, Pennell and Hobbs.)

Given a statistical structure 2 X, A, {Pθ : θ ∈ L}) and a probability measure µ on the parameter space (L, C), we consider a bayesian experiment (X × L, A ⊗ C, π). We concentrate our attention on the definitions of µ-a.e. dominated statistical structure and of dominated bayesian experiment. Under the assumption that A is countably generated, we prove that µ-a.e. domination of (X, A, {Pθ : θ ∈ L}) is equivalent to domination of (X × L, A ⊗ C, π). Furthermore, when (X, A, {Pθ : θ ∈ L}) is µ-a.e. dominated, the predictive probability is shown to be a possible µ-a.e. dominating measure.