Search

Aims: To present alternative approaches related to both structural assumptions and data sources for the development of a decision analytic model for evaluating the cost-effectiveness of adjuvant nivolumab compared with surveillance in patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection. Methods and results: Alternative approaches related to both structural assumptions and data sources are presented to address challenges and data gaps, as well as discussion of strengths and limitations of each approach. Specifically, challenges and considerations related to the following are presented: (1) selection of a modeling approach (partitioned survival model or state transition model) given the available evidence, (2) choice of health state structure (three- or four-state) to model disease progression and subsequent therapy, (3) modeling of outcomes from subsequent therapy using tunnel states to account for time-dependent transition probabilities or absorbing health states with one-off costs and outcomes applied, and (4) methods for modeling health-state transitions in a setting where treatment has curative intent and available survival data are immature. Conclusions: Multiple considerations must be taken into account when developing an economic model for new, emerging oncology treatments in early lines of therapy, all of which can affect the model's overall ability to estimate (quality-adjusted) survival benefits over a lifetime horizon. This paper identifies a series of key structural and analytic considerations regarding modeling of nivolumab treatment in the adjuvant MIUC setting. Several alternative approaches with regard to structure and data have been included in a flexible cost-effectiveness model so the impact of the alternative approaches on model results can be explored. The impact of these alternative approaches on cost-effectiveness results are presented in a companion article. Our findings may also help inform the development of future models for other treatments and settings in early-stage cancer. [ABSTRACT FROM AUTHOR]

Aim: National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an STM are optimized to provide closer estimates of the within-trial endpoints. Materials & methods: An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model elected based on AIC/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan–Meier curves. Sensitivity analyses were conducted to assess uncertainty. Results: The novel approach resulted in closer estimations to the OS and PFS Kaplan–Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed. Conclusion: A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.

Background: Little is known about disease trajectories for men with castration-resistant prostate cancer (CRPC). Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes. Design, setting, and participants: The model was generated using population-based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state. Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category. Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data. Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category. Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.

Deep reinforcement learning (DRL) as an important learning paradigm in the field of machine learning, has received increasing attentions after AlphaGo defeats the human. DRL interacts with the environment by trials and errors, and obtains the optimal policy by maximizing the cumulative reward. Reinforcement learning can be divided into two categories: model-free reinforcement learning and model-based reinforcement learning. The tra-ining process of model-free reinforcement learning needs a large number of samples. It is difficult for model-free reinforcement learning to get good performance when the sampling budget is limited, and a large number of samples cannot be collected. However, model-based reinforcement learning can reduce the real sample demand and improve the data efficiency through making full use of the environment model. This paper focuses on the field of model-based reinforcement learning, introduces its research status, investigates its classical algorithms, and discusses?future development trend and application prospect.

An integrated Matlab model comprising of an electro-mechanical actuator (EMA) model, load model and state model has been constructed to investigate the cargo door actuator motion control, operation and working states, of which, a Simulink® servomotor system model is adopted to mimic the EMA actuator, a SimMechanics® model is used to compute the actuator's load, a Stateflow® state transition model is employed to simulate the working process. The results indicated that the motion is well controlled, generated load force is valid, working states and their transition logics are reasonable. The simulation verified the effectiveness and easiness of the integrated modelling method.

Objectives: Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs).Methods: In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice.Results: PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states.Conclusions: The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival. [ABSTRACT FROM AUTHOR]

Background: Up to 31% of patients with relapsing-remitting multiple sclerosis (RRMS) discontinue treatment with disease-modifying drug (DMD) within the first year, and of the patients who do continue, about 40% are nonadherent. Shared decision making may decrease nonadherence and discontinuation rates, but evidence in the context of RRMS is limited. Shared decision making may, however, come at additional costs. This study aimed to explore the potential cost-effectiveness of shared decision making for RRMS in comparison with usual care, from a (limited) societal perspective over a lifetime. Methods: An exploratory economic evaluation was conducted by adapting a previously developed state transition model that evaluates the cost-effectiveness of a range of DMDs for RRMS in comparison with the best supportive care. Three potential effects of shared decision making were explored: 1) a change in the initial DMD chosen, 2) a decrease in the patient's discontinuation in using the DMD, and 3) an increase in adherence to the DMD. One-way and probabilistic sensitivity analyses of a scenario that combined the 3 effects were conducted. Results: Each effect separately and the 3 effects combined resulted in higher quality-adjusted life years (QALYs) and costs due to the increased utilization of DMD. A decrease in discontinuation of DMDs influenced the incremental cost-effectiveness ratio (ICER) most. The combined scenario resulted in an ICER of €17,875 per QALY gained. The ICER was sensitive to changes in several parameters. Conclusion: This study suggests that shared decision making for DMDs could potentially be cost-effective, especially if shared decision making would help to decrease treatment discontinuation. Our results, however, may depend on the assumed effects on treatment choice, persistence, and adherence, which are actually largely unknown. [ABSTRACT FROM AUTHOR]

An integrated Matlab model comprising of an electro-mechanical actuator (EMA) model, load model and state model has been constructed to investigate the cargo door actuator motion control, operation and working states, of which, a Simulink® servomotor system model is adopted to mimic the EMA actuator, a SimMechanics® model is used to compute the actuator's load, a Stateflow® state transition model is employed to simulate the working process. The results indicated that the motion is well controlled, generated load force is valid, working states and their transition logics are reasonable. The simulation verified the effectiveness and easiness of the integrated modelling method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ack‐storm DoS attacks are injection attacks against an active Transmission Control Protocol (TCP) connection. These attacks can be generated by a very weak adversary and can generate amplification factor of orders of magnitude by exploiting a weakness in the TCP protocol specification. This attack requires sending two packets by the adversary with acknowledgement number greater than the sequence number used in each direction and the two end hosts will attempt to re‐synchronise the sequence numbers by sending duplicate acknowledgement and enter a loop. In this study, the authors propose a state transition model based detection scheme to detect these DoS attacks. This state transition machine called constrained counting automata (CCA) has the ability to count the number of times a state has been revisited and its transitions are constrained by invariant conditions to be satisfied. They model the chances of receiving a packet with acknowledgement number greater than the sequence number used by its peer as a probability distribution and use it to set appropriate value of threshold on revisits of a state for detecting attack. By experimenting within a local network and in Internet, they show that CCA can detect Ack‐storm DoS attacks. [ABSTRACT FROM AUTHOR]

Objective: Vagus nerve stimulation (VNS) is a potential therapeutic approach in a number of clinical applications. Although VNS is commonly delivered in an open-loop approach, it is now recognized that closed-loop stimulation may be necessary to optimize the therapy. In this paper, we propose an original generic closed-loop control system that can be readily integrated into an implantable device and allows for the adaptive modulation of multiple VNS parameters. Methods: The proposed control method consists of a state transition model (STM), in which each state represents a set of VNS parameters, and a state transition algorithm that optimally selects the best STM state, minimizing the error between an observed physiological variable and a given target value. The proposed method has been integrated into a real-time adaptive VNS prototype system and has been applied here to the regulation of the instantaneous heart rate, working synchronously with cardiac cycles. A quantitative performance evaluation is performed on seven sheep by computing classical control performance indicators. A comparison with a proportional–integral (PI) controller is also performed. Results: The STM controller presents a median mean square error, overshoot, and settling time, respectively, equal to 622.21 ms $^2$ , 72.8%, and 7.5 beats. Conclusion: The proposed control method yields satisfactory accuracy and time response, while presenting a number of benefits over classical PI controllers. It represents a feasible approach for multiparametric VNS control on implantable devices. Significance: Closed-loop multiparametric stimulation may improve response and minimize side effects on current pathologies treated by VNS. [ABSTRACT FROM AUTHOR]

Objective: To evaluate long-term health benefits and risks of adding vorapaxar (VOR) to the standard care antiplatelet therapy (SC) of aspirin and/or clopidogrel, among a population with a recent myocardial infarction (MI) and/or peripheral artery disease (PAD).Research Design and Methods: In a state-transition model, patients transition between health states (event-free, recurrent MI, stroke, death), while at risk of experiencing non-transition-related revascularization and non-fatal bleeding events. Risk equations developed from the TRA 2°P-TIMI 50 trial's patient-level data were used to predict cardiovascular (CV) outcomes over longer time horizons. Additional sources, including trials and US-based observational studies, informed the inputs for short-term CV risk, non-CV death, and health-related quality of life. Survival and quality-adjusted life-years (QALYs) were estimated over a lifetime horizon, discounted at 3% per year.Results: Within a cohort of 7361 patients with recent MI and/or PAD, VOR + SC relative to SC alone yielded 176 fewer CV events (MIs, strokes, or CV deaths), but 27 more major bleeding events. VOR + SC was associated with increased life expectancy and health benefits (19.93 undiscounted life-years [LYs], 9.57 discounted QALYs vs. 19.61 undiscounted LYs, 9.41 discounted QALYs). The results were most sensitive to scenarios varying time of vorapaxar initiation, and the assumptions in the 90 day period post-MI. Additional analyses showed that add-on vorapaxar provides consistent incremental benefits in high-risk subgroups.Conclusion: This study contributes to the growing literature on secondary prevention add-on therapy, as results from these modeling analyses suggest that adding vorapaxar to SC for patients at high atherothrombotic risk can provide long-term health benefits. [ABSTRACT FROM AUTHOR]

Background: The presence of comorbid conditions is strongly related to survival and also affects treatment choices in cancer patients. This comorbidity is often quantified by the Charlson Comorbidity Index (CCI) using specific weights (1, 2, 3, or 6) for different comorbidities. It has been shown that the CCI increases at different times and with different sizes, so that traditional time to event analysis is not adequate to assess these temporal changes. Here, we present a method to model temporal changes in CCI in cancer patients using data from PCBaSe Sweden, a nation-wide population-based prospective cohort of men diagnosed with prostate cancer. Our proposed model is based on the assumption that a change in comorbidity, as quantified by the CCI, is an irreversible one-way process, i.e., CCI accumulates over time and cannot decrease.Methods: CCI was calculated based on 17 disease categories, which were defined using ICD-codes for discharge diagnoses in the National Patient Register. A state transition model in discrete time steps (i.e., four weeks) was applied to capture all changes in CCI. The transition probabilities were estimated from three modelling steps: 1) Logistic regression model for vital status, 2) Logistic regression model to define any changes in CCI, and 3) Poisson regression model to determine the size of CCI change, with an additional logistic regression model for CCI changes ≥ 6. The four models combined yielded parameter estimates to calculate changes in CCI with their confidence intervals.Results: These methods were applied to men with low-risk prostate cancer who received active surveillance (AS), radical prostatectomy (RP), or curative radiotherapy (RT) as primary treatment. There were large differences in CCI changes according to treatment.Conclusions: Our method to model temporal changes in CCI efficiently captures changes in comorbidity over time with a small number of regression analyses to perform - which would be impossible with tradition time to event analyses. However, our approach involves a simulation step that is not yet included in standard statistical software packages. In our prostate cancer example we showed that there are large differences in development of comorbidities among men receiving different treatments for prostate cancer. [ABSTRACT FROM AUTHOR]

The demand for ambulatory monitoring is rising due to the widespread adoption of home-based healthcare models for the increasing elderly population. Since monitoring patients makes it possible to provide remote medical services based on real-world contexts, context-aware technology is regarded as a crucial element in core module development for ubiquitous healthcare solutions. This paper describes the design and implementation of a class label tagging system for human activity recognition based on sensor data from a single tri-axial accelerometer attached to the waist of a human subject. Our human activity and posture classifier (APC) was designed to model more effectively an event that takes place over a period of time. Consequently, the APC problem becomes the process of tagging class labels onto sequential data from a single tri-axial accelerometer. This sequential tagging naturally led to our hypothesis that a linear combination of the classification model and the prior, approximated by observing a state transition event of human activity and posture (AP), may decrease the error rate of the system. Therefore, this paper aims at testing the aforementioned experimental hypothesis while explicating our method of direct optimization, also known as direct loss minimization. Our experimental hypothesis is based on the view that the task of labeling a series of class tags on a data object of the AP event should be supported by a statistical model capable of robustly capturing the probabilistic context influencing the generation of a set of features at a given discrete time. A radial basis function network algorithm was used for parameter estimation of a class likelihood inference model augmented by training a state transition model to capture the sequential nature of a posture or activity generation event. The linear combination requires our design step to have a further optimization, in addition to learning the parameters of each inference model, of the model parameters that represent the relative contribution to the decision process. Previous studies have elaborated on an effective approach to direct optimization of model parameters when an appropriate evaluation metric exists for a given problem domain. In our case, f-measure was used as an objective function of the direct optimization. We used local beam search to optimize directly the weight vector for the likelihood and the prior. Our method is tested on the set of nine-dimensional feature vectors contained in 69,376 lines of sequential AP data. The experiment shows that embedding prior probability into the recognition process decreases the error rate in a set of categories. Several experiments proved the efficiency of our proposed method and the usefulness of the system. [ABSTRACT FROM AUTHOR]

IP-enabled sensors can be globally addressable by any Internet-connected entity, and therefore, their protection presents different challenges than that of traditional sensors, as they are subject to any potential attacker in the Internet. For this reason, specific security protocols must be developed to address the security requirements of IP-enabled sensors. An interesting approach to achieve this aim is the Ladon security protocol, which allows resource-deprived devices to efficiently implement end-to-end authentication, authorisation and key establishment mechanisms. However, in so limited environments such as sensor networks, not only efficient protocols must be defined, but they must also be optimally parameterised. This paper constitutes a step forward in this direction. First, a state transition model of the Ladon protocol is presented to analytically describe its behaviour. Then, this model is used to select the most effective parameterisation of the protocol in terms of message retransmissions and execution of cryptographic operations. The obtained results show that the selected parameterisation allows maximising the probability of a successful secure session establishment, while keeping the overhead introduced by the protocol low. Additionally, the performance comparison carried out shows that Ladon outperforms alternative approaches to achieve the same objective in terms of message transmission and reception operations. [ABSTRACT FROM AUTHOR]

In a population of chronic dialysis patients with an extensive burden of cardiovascular disease, estimation of the effectiveness of cardioprotective medication in the literature is based on calculation of a hazard ratio comparing hazard of mortality for two groups (with or without drug exposure) measured at a single point in time or through the cumulative metric of proportion of days covered (PDC) on medication. Though both approaches can be modeled in a time-dependent manner using a Cox regression model, we propose a more complete time-dependent metric for evaluating cardioprotective medication efficacy. We consider that drug effectiveness is potentially the result of interactions between three time-dependent covariate measures, current drug usage status (ON vs. OFF), proportion of cumulative exposure to drug at a given point in time, and the patient's switching behavior between taking and not taking the medication. We show that modeling of all three of these time-dependent measures illustrates more clearly how varying patterns of drug exposure affect drug effectiveness, which could remain obscured when modeled by the more standard single time-dependent covariate approaches. We propose that understanding the nature and directionality of these interactions will help the biopharmaceutical industry in better estimating drug efficacy. [ABSTRACT FROM PUBLISHER]

Abstract: The fundamental geomorphic responses to environmental change are qualitative changes in system states. This study is concerned with the complexity of state transition models (STM), and synchronization. The latter includes literal and inferential synchronization, the extent to which observations or relationships at one time period can be applied to others. Complexity concerns the extent to which STM structure may tend to amplify effects of change. Three metrics—spectral radius, Laplacian spectral radius, and algebraic connectivity—were applied to several generic geomorphic STMs, and to three real-world examples: the San Antonio River delta, soil transitions in a coastal plain agricultural landscape, and high-latitude thermokarst systems. While the Laplacian spectral radius was of limited use, spectral radius and algebraic complexity provide significant, independent information. The former is more sensitive to the intensity of cycles within the transition graph structure, and to the overall complexity of the STM. Spectral radius is an effective general index of graph complexity, and especially the likelihood of amplification and intensification of changes in environmental boundary conditions, or of the propagation of local disturbances within the system. The spectral radius analyses here illustrate that more information does not necessarily decrease uncertainty, as increased information often results in the expansion of state transition networks from simpler linear sequential and cyclic to more complex structures. Algebraic connectivity applied to landscape-scale STMs provides a measure of the likelihood of complex response, with synchronization inversely related to complex response. [Copyright &y& Elsevier]

A method is proposed in the present paper for analyzing associations based on a link structure. By means of this method, the effect that disabled target information in a linked information group has on the other information is calculated and the mutual association is quantified. An approach that is well known to biological experimentation is the knockout study whereby a specific gene is knocked out, that is, deleted, and symptoms are observed in an attempt to estimate gene function. The method proposed here can be regarded as one in which the basic concept of this knockout study is introduced as an information processing technique, with the state transition model being used to obtain the observation results. Also discussed is the application of this method to disease-related gene search in the field of life sciences, where knowledge discovery is very important. In the present paper, the well-known OMIM (Online Mendelian Inheritance in Man) was used as the database related to hereditary diseases in humans, and the effectiveness of the association analysis was proven through the results of experiments involving hypertension and other lifestyle-related diseases in which multiple genes play a complex role. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(5): 46–55, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20404 [ABSTRACT FROM AUTHOR]

This paper proposes a system architecture for event recognition that dynamically integrates information from multiple sources (e.g., multimodal data from visual and auditory sensors). The proposed system consists of multiple event classifiers called Continuous State Machines (CSMs). Each CSM has a state transition rule in a continuous state space and classifies time-varying patterns from a different single source. Since the rule is defined as an extension of Kalman filters (i.e., the next state is deduced from the trade-off scheme between the input data and the model's prediction), CSMs support dynamic time warping and robustness against noise. We then introduce an interaction method among CSMs to classify events from multiple sources. A continuous state space (i.e., vector space) allows us to design interaction as minimization of an energy function. This interaction enables the system to dynamically suppress unreliable classifiers and improves system reliability and the accuracy of classifying events in dynamically changing situations (e.g., the object is temporary occluded from one of multiple cameras in a gesture recognition task). Experimental results on gesture recognition by two cameras show the effectiveness of our proposed system. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(14): 1–12, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10507 [ABSTRACT FROM AUTHOR]

A protocol standard, in general, can lead to different implementations, which necessitates the need for conformance testing of an implementation to its standard. Testing is carried out with the help of a test sequence generated from a protocol specification. This paper presents a detailed study of four formal methods (T-, U-, D-, and W-methods) for generating test sequences for protocols. Applications of these methods to NBS Class 4 Transport Protocol are discussed. This paper also presents an estimation of fault coverage of four protocol test sequences generation techniques using Monte Carlo simulation. The ability of a test sequence to decide whether a protocol implementation conforms to its specification heavily relies upon the range of faults that it can capture. Conformance is defined at two levels, namely, weak and strong conformance. This study shows that a test sequence produced by T-method has a poor fault detection capability whereas test sequences produced by U-, D- and W-methods have comparable (superior to that for T-method) fault coverage on several classes of randomly generated machines used in this study. Also, some problems with a straightforward application of the four protocol test sequence generation methods to real-world communication protocols are pointed out. [ABSTRACT FROM AUTHOR]