Search

Statistical learning of the structures of cellular networks, such as protein signaling pathways, is a topical research field in computational systems biology. To get the most information out of experimental data, it is often required to develop a tailored statistical approach rather than applying one of the off-the-shelf network reconstruction methods. The focus of this paper is on learning the structure of the mTOR protein signaling pathway from immunoblotting protein phosphorylation data. Under two experimental conditions eleven phosphorylation sites of eight key proteins of the mTOR pathway were measured at ten non-equidistant time points. For the statistical analysis we propose a new advanced hierarchically coupled non-homogeneous dynamic Bayesian network (NH-DBN) model, and we consider various data imputation methods for dealing with non-equidistant temporal observations. Because of the absence of a true gold standard network, we propose to use predictive probabilities in combination with a leave-one-out cross validation strategy to objectively cross-compare the accuracies of different NH-DBN models and data imputation methods. Finally, we employ the best combination of model and data imputation method for predicting the structure of the mTOR protein signaling pathway. [ABSTRACT FROM AUTHOR]

Multi‐site MRI datasets are crucial for big data research. However, neuroimaging studies must face the batch effect. Here, we propose an approach that uses the predictive probabilities provided by Gaussian processes (GPs) to harmonize clinical‐based studies. A multi‐site dataset of 216 Parkinson's disease (PD) patients and 87 healthy subjects (HS) was used. We performed a site GP classification using MRI data. The outcomes estimated from this classification, redefined like Weighted HARMonization PArameters (WHARMPA), were used as regressors in two different clinical studies: A PD versus HS machine learning classification using GP, and a VBM comparison (FWE‐p <.05, k = 100). Same studies were also conducted using conventional Boolean site covariates, and without information about site belonging. The results from site GP classification provided high scores, balanced accuracy (BAC) was 98.39% for grey matter images. PD versus HS classification performed better when the WHARMPA were used to harmonize (BAC = 78.60%; AUC = 0.90) than when using the Boolean site information (BAC = 56.31%; AUC = 0.71) and without it (BAC = 57.22%; AUC = 0.73). The VBM analysis harmonized using WHARMPA provided larger and more statistically robust clusters in regions previously reported in PD than when the Boolean site covariates or no corrections were added to the model. In conclusion, WHARMPA might encode global site‐effects quantitatively and allow the harmonization of data. This method is user‐friendly and provides a powerful solution, without complex implementations, to clean the analyses by removing variability associated with the differences between sites. [ABSTRACT FROM AUTHOR]

Paired comparison (PC) scheme is very helpful in ranking objects. In this paper, a new paired comparison model is developed using the Weibull distribution and analyzed in Bayesian framework using informative prior under different loss functions. Summary of posterior estimates is evaluated to assess the ranking of the social media applications, i.e., Facebook, Whatsapp, Instagram, Skype and Twitter. The ranking presented by the model is justifiable according to the prevalence of the usage of the social media applications. The developed model is found to be plausible by the goodness of fit criteria.

Background: Bayesian methods, with the predictive probability (PredP), allow multiple interim analyses with interim posterior probability (PostP) computation, without the need to correct for multiple looks at the data. The objective of this paper was to illustrate the use of PredP by simulating a sequential analysis of a clinical trial.Methods: We used data from the Laryngobloc trial that planned to include 480 patients to demonstrate the equivalence of success between a laryngoscopy performed with the Laryngobloc® device and a control device. A crossover Bayesian design was used. The success rates of the two laryngoscopy devices were compared. Interim analyses, computed from random numbers of subjects, were simulated.Results: The PostP of equivalence rapidly reached the predefined bound of 0.95. The PredP computed with an equivalence margin of 10% reached the efficacy bound between 352 and 409 of the 480 included patients. If a frequentist analysis had been made on the basis of 217 out of 480 subjects, the study would have been prematurely stopped for equivalence. The PredP indicated that this result was nonetheless unstable and that the equivalence was, thus far, not guaranteed.Conclusions: Based on these interim analyses, we can conclude with a sufficiently high probability that the equivalence would have been met on the primary outcome before the predetermined end of this particular trial. If a Bayesian approach using PredP had been used, it would have allowed an early termination of the trial by reducing the calculated sample size by 15-20%. [ABSTRACT FROM AUTHOR]

Gibbs-type random probability measures, or Gibbs-type priors, are arguably the most "natural" generalization of the celebrated Dirichlet prior. Among them the two parameter Poisson–Dirichlet prior certainly stands out in terms of mathematical tractability and interpretability of its predictive probabilities, which made it the natural candidate in a plethora of applications. Given a random sample of size n from an arbitrary Gibbs-type prior, we show that the corresponding predictive probabilities admit a large n approximation, with an error term vanishing as o(1/n), which maintains the same desirable features as the predictive probabilities of the two parameter Poisson–Dirichlet prior. Our result is illustrated through an extensive simulation study, which includes an application in the context of Bayesian nonparametric mixture modeling. [ABSTRACT FROM AUTHOR]

Paired comparison (PC) scheme is very helpful in ranking objects. In this paper, a new paired comparison model is developed using the Weibull distribution and analyzed in Bayesian framework using informative prior under different loss functions. Summary of posterior estimates is evaluated to assess the ranking of the social media applications, i.e., Facebook, Whatsapp, Instagram, Skype and Twitter. The ranking presented by the model is justifiable according to the prevalence of the usage of the social media applications. The developed model is found to be plausible by the goodness of fit criteria. [ABSTRACT FROM AUTHOR]

Across the Higher Education (HE) sector student feedback is used to feed into university processes and guide decision making. In this study, data gathered allowed the authors to investigate a hitherto neglected, but important, cohort of successful students - those who succeeded when all the odds were stacked against them. The identified group of students had relatively low predictive probabilities of passing their module, and yet despite all the odds passed. These identified students were interviewed by phone which yielded a much richer data set than is often available from student surveys. The advice they considered to be important to future students included specific guidance to get ahead, to plan and to contact their own academic tutor. Based on this advice an opportunity for future students to have a flexible early start on a specific module was created. A 4% increase in the number of registered students who subsequently started the module was maintained through to module completion. [ABSTRACT FROM AUTHOR]

PurposeWe use the historical data from the European Study of Daily Fecundability and we develop an algorithm to determine the fertile window in a woman’s cycle according to the rules of the C.A.Me.N. symptothermal method proposed by the Centro Ambrosiano Metodi Naturali. Our aim is to identify variables acting on the probability of conception by considering the fertile window and factors that cannot be explained by employing the observed covariates of individuals and couples.MethodsWe adopt the latent Markov model with covariates tailored for data collected at times when a latent process detects the dependence across fertile periods of each woman’s cycle. We consider measurement errors, transitions between conception and non-conception, and the prediction of conception rate over the fertile windows.ConclusionWe find that the conception pattern is mainly related to sexual intercourse behavior during the fertile window and to previous pregnancies. For the cohort under study, we predict a steep decline in the average conception rate across fertile windows.

A fundamental problem in Bayesian nonparametrics consists of selecting a prior distribution by assuming that the corresponding predictive probabilities obey certain properties. An early discussion of such a problem, although in a parametric framework, dates back to the seminal work by English philosopher W. E. Johnson, who introduced a noteworthy characterization for the predictive probabilities of the symmetric Dirichlet prior distribution. This is typically referred to as Johnson’s “sufficientness” postulate. In this paper, we review some nonparametric generalizations of Johnson’s postulate for a class of nonparametric priors known as species sampling models. In particular, we revisit and discuss the “sufficientness” postulate for the two parameter Poisson–Dirichlet prior within the more general framework of Gibbs-type priors and their hierarchical generalizations. [ABSTRACT FROM AUTHOR]

Statistical learning of the structures of cellular networks, such as protein signaling pathways, is a topical research field in computational systems biology. To get the most information out of experimental data, it is often required to develop a tailored statistical approach rather than applying one of the off-the-shelf network reconstruction methods. The focus of this paper is on learning the structure of the mTOR protein signaling pathway from immunoblotting protein phosphorylation data. Under two experimental conditions eleven phosphorylation sites of eight key proteins of the mTOR pathway were measured at ten non-equidistant time points. For the statistical analysis we propose a new advanced hierarchically coupled non-homogeneous dynamic Bayesian network (NH-DBN) model, and we consider various data imputation methods for dealing with non-equidistant temporal observations. Because of the absence of a true gold standard network, we propose to use predictive probabilities in combination with a leave-one-out cross validation strategy to objectively cross-compare the accuracies of different NH-DBN models and data imputation methods. Finally, we employ the best combination of model and data imputation method for predicting the structure of the mTOR protein signaling pathway., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Background: The 'Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT)' project is a collaborative effort supported by the National Institutes of Health (NIH) and United States Food & Drug Administration (FDA) to explore how adaptive clinical trial design might improve the evaluation of drugs and medical devices. ADAPT-IT uses the National Institute of Neurologic Disorders & Stroke-supported Neurological Emergencies Treatment Trials (NETT) network as a 'laboratory' in which to study the development of adaptive clinical trial designs in the confirmatory setting. The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial was selected for funding by the NIH-NINDS at the start of ADAPT-IT and is currently an ongoing phase III trial of tight glucose control in hyperglycemic acute ischemic stroke patients. Within ADAPT-IT, a Bayesian adaptive Goldilocks trial design alternative was developed. Methods: The SHINE design includes response adaptive randomization, a sample size re-estimation, and monitoring for early efficacy and futility according to a group sequential design. The Goldilocks design includes more frequent monitoring for predicted success or futility and a longitudinal model of the primary endpoint. Both trial designs were simulated and compared in terms of their mean sample size and power across a range of treatment effects and success rates for the control group. Results: As simulated, the SHINE design tends to have slightly higher power and the Goldilocks design has a lower mean sample size. Both designs were tuned to have approximately 80% power to detect a difference of 25% versus 32% between control and treatment, respectively. In this scenario, mean sample sizes are 1,114 and 979 for the SHINE and Goldilocks designs, respectively. Conclusions: Two designs were brought forward, and both were evaluated, revised, and improved based on the input of all parties involved in the ADAPT-IT process. However, the SHINE investigators were tasked with choosing only a single design to implement and ultimately elected not to implement the Goldilocks design. The Goldilocks design will be retrospectively executed upon completion of SHINE to later compare the designs based on their use of patient resources, time, and conclusions in a real world setting. [ABSTRACT FROM AUTHOR]

We present a Bayesian adaptive design for a confirmatory trial to select a trial’s sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. We refer to this as a Goldilocks trial design, as it is constantly asking the question, “Is the sample size too big, too small, or just right?” We describe the adaptive sample size algorithm, describe how the design parameters should be chosen, and show examples for dichotomous and time-to-event endpoints. [ABSTRACT FROM AUTHOR]

Background Announcements of interim analyses of a clinical trial convey information about the results beyond the trial’s Data Safety Monitoring Board (DSMB). The amount of information conveyed may be minimal, but the fact that none of the trial’s stopping boundaries has been crossed implies that the experimental therapy is neither extremely effective nor hopeless. Predicting success of the ongoing trial is of interest to the trial’s sponsor, the medical community, pharmaceutical companies, and investors. We determine the probability of trial success by quantifying only the publicly available information from interim analyses of an ongoing trial. We illustrate our method in the context of the National Surgical Adjuvant Breast and Bowel (NSABP) trial, C-08. Methods We simulated trials based on the specifics of the NSABP C-08 protocol that were publicly available. We quantified the uncertainty around the treatment effect using prior weights for the various possibilities in light of other colon cancer studies and other studies of the investigational agent, bevacizumab. We considered alternative prior distributions. Results Subsequent to the trial’s third interim analysis, our predictive probabilities were: that the trial would eventually be successful, 48.0%; would stop for futility, 7.4%; and would continue to completion without statistical significance, 44.5%. The actual trial continued to completion without statistical significance. Conclusions Announcements of interim analyses provide information outside the DSMB’s sphere of confidentiality. This information is potentially helpful to clinical trial prognosticators. ‘Information leakage’ from standard interim analyses such as in NSABP C-08 is conventionally viewed as acceptable even though it may be quite revealing. Whether leakage from more aggressive types of adaptations is acceptable should be assessed at the design stage. [ABSTRACT FROM AUTHOR]

We present a probabilistic and a possibilistic model for assessing the risk of a service level agreement for a computing task in a cluster/grid environment. These models can also be applied to cloud computing. Using the predictive probabilistic approach we develop a framework for resource management in grid computing, and by introducing an upper limit for the number of failures we approximate the probability that a particular computing task is successful. In the predictive possibility model we estimate the possibility distribution of the future number of node failures by a fuzzy nonparametric regression technique. Then the resource provider can use the probabilistic or the possibilistic model to get alternative risk assessments. [ABSTRACT FROM AUTHOR]

P. R. Killeen's (2005a) probability of replication (prep) of an experimental result is the fiducial Bayesian predictive probability of finding a same-sign effect in a replication of an experiment. prep is now routinely reported in Psychological Science and has also begun to appear in other journals. However, there is little concrete, practical guidance for use of prep, and the procedure has not received the scrutiny that it deserves. Furthermore, only a solution that assumes a known variance has been implemented. A practical problem with prep is identified: In many articles, prep appears to be incorrectly computed, due to the confusion between 1-tailed and 2-tailed p values. Experimental findings reveal the risk of misinterpreting prep as the predictive probability of finding a same-sign and significant effect in a replication (psrep). Conceptual and practical guidelines are given to avoid these pitfalls. They include an extension to the case of unknown variance. Moreover, other uses of fiducial Bayesian predictive probabilities for analyzing, designing ("how many subjects?"), and monitoring ("when to stop?") experiments are presented. Concluding remarks emphasize the role of predictive procedures in statistical methodology. [ABSTRACT FROM AUTHOR]

Gender, age, waist-hip ratio (WHR) and body mass index (BMI) have been reported to herald two complications of obesity: diabetes or hypertension. Most literature is based on patient populations with mean BMIs ranging from 22 to 35 kg/m. This study population of severely obese patients selected for surgical treatment of obesity had a mean age of 37 ± 9.4 years, median WHR of 0.9 (0.4-2.1) and median BMI of 44.0 (29-89) kg/m. It was found that age, WHR and BMI were significant predictors for diabetes or hypertension. Gender was not found to be a significant predictor for either diabetes or hypertension. A mathematical model was used to calculate the predictive probability of developing diabetes or hypertension using age, BMI and WHR. [ABSTRACT FROM AUTHOR]

Purpose: We use the historical data from the European Study of Daily Fecundability and we develop an algorithm to determine the fertile window in a woman's cycle according to the rules of the C.A.Me.N. symptothermal method proposed by the Centro Ambrosiano Metodi Naturali. Our aim is to identify variables acting on the probability of conception by considering the fertile window and factors that cannot be explained by employing the observed covariates of individuals and couples., Methods: We adopt the latent Markov model with covariates tailored for data collected at times when a latent process detects the dependence across fertile periods of each woman's cycle. We consider measurement errors, transitions between conception and non-conception, and the prediction of conception rate over the fertile windows., Conclusion: We find that the conception pattern is mainly related to sexual intercourse behavior during the fertile window and to previous pregnancies. For the cohort under study, we predict a steep decline in the average conception rate across fertile windows.