Bayesian inference using Hamiltonian Monte-Carlo algorithm for nonlinear joint modeling in the context of cancer immunotherapy.

Given a joint model and its parameters, Bayesian individual dynamic prediction (IDP) of biomarkers and risk of event can be performed for new patients at different landmark times using observed biomarker values. The aim of the present study was to compare IDP, with uncertainty, using Stan 2.18, Monolix 2018R2 and NONMEM 7.4. Simulations of biomarker and survival were performed using a nonlinear joint model of prostate-specific antigen (PSA) kinetics and survival in metastatic prostate cancer. Several scenarios were evaluated, according to the strength of the association between PSA and survival. For various landmark times, a posteriori distribution of PSA kinetic individual parameters was estimated, given individual observations, with each software. Samples of individual parameters were drawn from the posterior distribution. Bias and imprecision of individual parameters as well as coverage of 95% credibility interval for PSA and risk of death were evaluated. All software performed equally well with small biases on individual parameters. Imprecision on individual parameters was comparable across software and showed marked improvements with increasing landmark time. In terms of coverage, results were also comparable and all software were able to well predict PSA kinetics and survival. As for computing time, Stan was faster than Monolix and NONMEM to obtain individual parameters. Stan 2.18, Monolix 2018R2 and NONMEM 7.4 are able to characterize IDP of biomarkers and risk of event in a nonlinear joint modelling framework with correct uncertainty and hence could be used in the context of individualized medicine.

This article reviews the Bayesian inference with the Monte Carlo Markov Chain (MCMC) and the Hamiltonian Monte Carlo (HMC) samplers as a competitor of the classical likelihood statistical inference for pharmacometricians. The MCMC and the HMC samplers have greatly contributed to realization of the Bayesian methods with minimal requirement of mathematical theory. They do not require any closed form of the posterior density nor linear approximation of complex nonlinear models in high dimension even with non-conjugate priors. The HMC even weakens the dependency of the chain and improves computational efficiency. Pharmacometrics is one of great beneficiaries since they use complex multivariate multilevel nonlinear mixed effects models based on the restricted maximum likelihood estimation. Comprehension of the Bayesian approach will help pharmacometricians to access the data analysis more conveniently.

Pushover analysis as known as non-linear static analysis is one of the structural analysis procedures that is widely used in seismic assessment and performance evaluation of existing structures . In the nonlinear analysis procedure, the behavior of the structure due to external loads acting can be seen from the status of the joints formed. One of the joint models that is less commonly used in non-linear static analysis is the fiber hinge joint modeling. Fiber hinge is a nonlinear joint model that can consider distributed plasticity along with its structural elements. Study this learn possibility difference in results analysis use fiber hinge because properties component specified nonlinear _ researcher . In order to study the impact of the use of joint modeling variations in nonlinear analysis, a regular reinforced concrete structure model with 4 height variations namely 3, 4, 5, and 6 floors which were analyzed with two different types of joint modeling. Fiber hinges and conventional lumped hinges will be defined in the cross section. Research results show that the use of fiber hinge in each model produces score relative moment _ small however given rotation _ relatively more big than lumped hinge as well relative base shear value small than lumped hinges. Considering the fiber hinge is method that gives state of natural response so that deformation globally generated _ more big than lumped hinges. This thing take effect to determination structure performance value.

Bayesian designs are increasingly used to conduct phase II clinical trials. However, stopping boundaries in most Bayesian designs are defined from posterior credible intervals. The use of designs based on posterior credible intervals results in a loss of efficiency when compared to formal stopping rules based on Bayesian hypothesis tests. Such designs also introduce an unnecessary element of subjectivity in the interpretation of trial results. We derive a new class of Bayesian designs based on formal hypothesis tests. The prior densities used to define the alternative hypotheses in these tests assign no mass to parameter values that are consistent with the null hypotheses and are called nonlocal alternative prior densities. We show that Bayesian designs based on hypothesis tests and nonlocal alternative prior densities are more efficient than common Bayesian designs based on posterior credible intervals and common frequentist designs. In contrast to trial designs based on Bayesian credible intervals, we demonstrate that the mis-specification of the prior densities used to describe the anticipated effect of the experimental treatment in designs based on hypothesis tests cannot increase the expected weight of evidence in favor of the trial agent. Extension of test-based designs to phase I-II designs and randomized phase II designs remains an open research question. Phase II single-arm trials designed using Bayesian hypothesis tests with nonlocal alternatives provide better operating characteristics, use fewer patients per correct decision, and provide more directly interpretable results than other commonly used Bayesian and frequentist designs. Because the mis-specification of the prior density on the effect of the experimental agent decreases the expected weight of evidence that is collected in favor of the experimental treatment, the use of Bayesian hypothesis tests to design clinical trials also eliminates a potential source of bias often associated with trials designed using posterior credible intervals.

Many engineered structures are assembled using different kinds of joints such as bolted, riveted and clamped joints. Even if joints are often a small part of the overall structure, they can have a massive impact on its dynamics due to the introduction of nonlinearities. Thus, joints are considered a design liability. Significant effort has been spent in joint characterization and modelling, but a predictive joint model is still non-existent. To overcome these uncertainties and ensure certain safety standards, joints are usually overdesigned according to static considerations and their stiffness. Especially damping and nonlinearity are not considered during the design process. This can lead to lower performance, lower payload, and as result of the joints structural dynamic models often do a poor job of predicting the dynamic response. However, it is well-known that, particularly for metal structures, joints represent the main source of energy dissipation. In this work a minimal model is used to show how structural performance can be improved using joints as a design variable. Common optimization tools are applied to a nonlinear joint model in order to damp undesired structural vibrations. Results illustrate how the intentional choice of joint parameters and locations can effectively reduce vibration level for a given operating point of a jointed structure.

Phylogenetic comparative methods correct for shared evolutionary history among a set of nonindependent organisms by modeling sample traits as arising from a diffusion process along the branches of a possibly unknown history. To incorporate such uncertainty, we present a scalable Bayesian inference framework under a general Gaussian trait evolution model that exploits Hamiltonian Monte Carlo (HMC). HMC enables efficient sampling of the constrained model parameters and takes advantage of the tree structure for fast likelihood and gradient computations, yielding algorithmic complexity linear in the number of observations. This approach encompasses a wide family of stochastic processes, including the general Ornstein–Uhlenbeck (OU) process, with possible missing data and measurement errors. We implement inference tools for a biologically relevant subset of all these models into the BEAST phylogenetic software package and develop model comparison through marginal likelihood estimation. We apply our approach to study the morphological evolution in the superfamily of Musteloidea (including weasels and allies) as well as the heritability of HIV virulence. This second problem furnishes a new measure of evolutionary heritability that demonstrates its utility through a targeted simulation study.

In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra-patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter-lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow-up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancerpatients.

Comparison of three in-house ELISAs for the detection of hepatitis E virus infection in pigs under field conditions

Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data

For colorectal surgery patients, prehabilitation can address common challenges in recovery, including muscle wasting, malnutrition, and emotional stress. This study aim to determine the impact of prehabilitation for patients with after colorectal surgery. We searched PubMed, Embase and Cochrane Library from inception up to April 2024. Two authors assessed the risk of bias by the Cochrane Risk of Bias tool-version 2. We computed mean difference (MD) on the basis of change values, with 95% credible interval (CI), for continuous outcomes (length of hospital stays (LOS and 6-min walk test (6MWT)), and risk ratios (RR) for categorical outcomes (number of patients with a complication). We conducted random effects meta-analysis within a Bayesian framework in R (RStudio, Boston, MA) and assessed the certainty of evidence using the grading of recommendations assessment, development and evaluation. Thirteen randomized controlled trials (RCTs) published were identified. Low-certainty evidence demonstrated that prehabilitation of exercise (3 studies; 185 patients; MD = 0.84, 95% CI-0.57 to 2.24), nutrition (3 studies; 279 patients; MD = -0.19, 95% CI -1.77 to 1.38), and exercise + nutrition (1 study; 44 patients; MD = -1.0, 95% CI -3.07 to 1.07), which may not differently when compared to standard care for LOS. Moderate-certainty evidence demonstrated that prehabilitation of exercise + nutrition + psychosocial (3 studies; 487 patients; MD = 51.57, 95% CI 14.39 to 88.76) probably increase patients' 6WMT. Low or very low certainty evidence demonstrated that prehabilitation of exercise (2 studies; 164 patients; RR = 0.86, 95% CI 0.47 to 1.59), nutrition (3 studies; 279 patients; RR = 1.05, 95% CI 0.79 to 1.41), exercise + nutrition (1 study; 44 patients; RR = 0.38, 95% CI 0.13 to 1.13), and exercise + nutrition + psychosocial (4 studies; 408 patients; RR = 0.77, 95% CI 0.6 to 1.0), which may not differently when compared to standard care for reduce patients' with a complication. The moderate-certainty evidence supporting an increase in 6MWT with the exercise + nutrition + psychosocial intervention highlights a potential benefit of this comprehensive approach; however, most comparisons are informed by limited data, yielding low to very low certainty evidence.

Simple SummaryNeoadjuvant chemotherapy is used in patients with initially unresectable advanced ovarian cancer (AOC) to reduce the disease bulk. The CA-125 level depends on tumor burden changes. A joint model (JM) is a statistical tool used for dynamic prediction during follow-up. A JM of longitudinal CA-125 was assessed as a reliable predictive model for overall and free disease survivals. We developed a dynamic and individual model to predict complete resectability of AOC using patients’ and tumor characteristics combined with kinetics of CA-125 during neo-adjuvant chemotherapy.In patients with advanced ovarian cancer (AOC) receiving neoadjuvant chemotherapy (NAC), predicting the feasibility of complete interval cytoreductive surgery (ICRS) is helpful and may avoid unnecessary laparotomy. A joint model (JM) is a dynamic individual predictive model. The aim of this study was to develop a predictive JM combining CA-125 kinetics during NAC with patients’ and clinical factors to predict resectability after NAC in patients with AOC. A retrospective study included 77 patients with AOC treated with NAC. A linear mixed effect (LME) sub-model was used to describe the evolution of CA-125 during NAC considering factors influencing the biomarker levels. A Cox sub-model screened the covariates associated with resectability. The JM combined the LME sub-model with the Cox sub-model. Using the LME sub-model, we observed that CA-125 levels were influenced by the number of NAC cycles and the performance of paracentesis. In the Cox sub-model, complete resectability was associated with Performance Status (HR = 0.57, [0.34–0.95], p = 0.03) and the presence of peritoneal carcinomatosis in the epigastric region (HR = 0.39, [0.19–0.80], p = 0.01). The JM accuracy to predict complete ICRS was 88% [82–100] with a predictive error of 2.24% [0–2.32]. Using a JM of a longitudinal CA-125 level during NAC could be a reliable predictor of complete ICRS.

Whose goals determine care?

The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using the idea of a kernel-based implicit nonlinear mapping to a high-dimensional feature space in which linear model formulations are specified. Practical expressions for the nonlinear regression are obtained in terms of the positive definite kernel function by solving a linear system. The nonlinear least squares support vector machine model is designed within the Bayesian evidence framework that allows us to find appropriate trade-offs between model complexity and in-sample model accuracy. From straightforward primal–dual reasoning, the Bayesian framework allows us to derive error bars on the prediction in a similar way as for linear models and to perform hyperparameter and input selection. Starting from the results of the linear modelling analysis, the Bayesian kernel-based prediction is successfully applied to out-of-sample prediction of an aggregated equity price index for the European chemical sector. Copyright © 2006 John Wiley & Sons, Ltd.

AimTo systematically compare the efficacy and safety of subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT) in children with allergic rhinitis (AR).MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched from inception to March 2, 2023. Outcomes included symptom scores (SSs), medication scores (MSs), symptom and medication scores (SMSs), new sensitizations, development of asthma, improvement, and treatment-related adverse events (TRAEs). The quality of the included studies was assessed by the modified Jadad scale and Newcastle-Ottawa scale (NOS). Meta-regression was carried out to explore the source of heterogeneity. Subgroup analysis was further conducted in terms of study design [randomized controlled trials (RCTs), cohort studies], allergen [house dust mites (HDMs), grass pollen], treatment duration (≥ 24, 12-23 or < 12 months), allergen immunotherapy (AIT) modality (drops or tablets), and AIT protocol [continuous, pre-seasonal, co-seasonal, or after the grass pollen season (GPS)]. Sensitivity analysis was conducted for all outcomes. A Bayesian framework and a Monte Carlo Markov Chain (MCMC) model were developed for indirect comparison.ResultsTotally 50 studies with 10813 AR children were included, with 4122 treated with SLIT, 1852 treated with SCIT, and 4839 treated with non-SLIT or non-SCIT therapy. For direct comparison, the SLIT group had a similar SS to the SCIT group [pooled standardized mean difference (SMD): 0.41, 95% confidence interval (CI): -0.46, 1.28, P = 0.353]. Comparable MSs were observed in the SLIT and SCIT groups (pooled SMD: 0.82, 95%CI: -0.88, 2.53, P = 0.344). For indirect comparison, no significant differences were found in SSs (pooled SMD: 1.20, 95% credibility interval (CrI): -1.70, 4.10), MSs (pooled SMD: 0.57, 95%CrI: -1.20, 2.30), SMSs (pooled SMD: 1.80, 95%CrI: -0.005, 3.60), new sensitizations [pooled relative risk (RR): 0.34, 95%CrI: 0.03, 3.58], and development of asthma (pooled RR: 0.68, 95%CrI: 0.01, 26.33) between the SLIT and SCIT groups; the SLIT group illustrated a significantly lower incidence of TRAEs than the SCIT group (pooled RR: 0.17, 95%CrI: 0.11, 0.26).ConclusionConsidering both efficacy and safety, SLIT might be a more favorable AIT than SCIT in the treatment of pediatric AR, which may serve as a decision-making reference for clinicians.Systematic review registrationPROSPERO (CRD42023460693).

In medical studies, a disease’s progress is often monitored through indicators that signify the improved or worsened condition of the patient. These are known as longitudinal biomarkers, which are observed along with an event of importance. Together, they form the framework of joint modeling, which has a longitudinal process and an inherently associated time-to-event process. In clinical studies, the change in biomarkers is often monitored in the form of a change in the patient’s plasma level after a drug is administered to the patient. Again, in such studies, patients also withdraw from the trials prematurely or at a later phase, thus giving rise to dropouts. In most cases, this dropout is not random (Missing Not at Random). A joint model has been considered to incorporate this informative dropout in longitudinal response. To demonstrate this approach, a one-compartmental pharmacokinetic (PK) nonlinear mixed-effects model consisting of time-dependent parameters has been used in this work. The dropout mechanism has been introduced using a proportional hazard model. A Bayesian model framework is adopted to study the model’s performance through detailed simulation. A PK study on the drug Divalproex subject to an informative dropout model has been discussed. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 97-110.