Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation

Background The expected value of sample information (EVSI) calculates the value of collecting additional information through a research study with a given design. However, standard EVSI analyses do not account for the slow and often incomplete implementation of the treatment recommendations that follow research. Thus, standard EVSI analyses do not correctly capture the value of the study. Previous research has developed measures to calculate the research value while adjusting for implementation challenges, but estimating these measures is a challenge. Methods Based on a method that assumes the implementation level is related to the strength of evidence in favor of the treatment, 2 implementation-adjusted EVSI calculation methods are developed. These novel methods circumvent the need for analytical calculations, which were restricted to settings in which normality could be assumed. The first method developed in this article uses computationally demanding nested simulations, based on the definition of the implementation-adjusted EVSI. The second method is based on adapting the moment matching method, a recently developed efficient EVSI computation method, to adjust for imperfect implementation. The implementation-adjusted EVSI is then calculated with the 2 methods across 3 examples. Results The maximum difference between the 2 methods is at most 6% in all examples. The efficient computation method is between 6 and 60 times faster than the nested simulation method in this case study and could be used in practice. Conclusions This article permits the calculation of an implementation-adjusted EVSI using realistic assumptions. The efficient estimation method is accurate and can estimate the implementation-adjusted EVSI in practice. By adapting standard EVSI estimation methods, adjustments for imperfect implementation can be made with the same computational cost as a standard EVSI analysis. Highlights Standard expected value of sample information (EVSI) analyses do not account for the fact that treatment implementation following research is often slow and incomplete, meaning they incorrectly capture the value of the study. Two methods, based on nested Monte Carlo sampling and the moment matching EVSI calculation method, are developed to adjust EVSI calculations for imperfect implementation when the speed and level of the implementation of a new treatment depends on the strength of evidence in favor of the treatment. The 2 methods we develop provide similar estimates for the implementation-adjusted EVSI. Our methods extend current EVSI calculation algorithms and thus require limited additional computational complexity.

Integrated optimization of production planning and scheduling in uncertain re-entrance environment for fixed-position assembly workshops

For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm.

Finding the parameters of a Bernoulli-Gaussian Model

<div><div><div><p>A transmission medium perturbed by an additive noise from which the estimated noise power is all information known, is better modeled as a Gaussian channel. Since the Gaussian channel is, according to Information Theory, the worst channel to transmit information through, this is the most pessimistic assumption. When noise samples are available though, choosing to model the transmission medium using a more sophisticated model pays off. The Bernoulli-Gaussian channel, would be one such a choice. Finding the three parameters that characterize the Bernoulli-Gaussian stochastic process which mathematically models the noise is a task of paramount importance. Many algorithms can be used to estimate the parameters of this model based on numerical methods. In the current work a closed form expression to estimate the model parameters is presented. All that is required besides the estimation of the power of Bernoulli-Gaussian process from the available noise samples is the estimation of two additional quantities: the expected value of the absolute value of the amplitude of the process—the first absolute moment—plus the third absolute moment, viz., the expected value of the third power of the absolute value of the process. An alternative option, often used for power line communication, is to model the transmission medium as a channel in which the noise is represented by a three parameter stochastic process called Middleton Class A. Other models (like generalized-Bernoulli-Gaussian, or Bernoulli- Gaussian with memory) might render a better medium model than the Bernoulli-Gaussian channel. Estimating the parameters of these processes is however a cumbersome task and, as we show in the current work, the rate harvested by using the simple, yet more sophisticated, Bernoulli-Gaussian channel is increased as compared to the, more pessimistic, Gaussian channel, allowing one thus to more closely approach the true capacity. The communication system design can be much improved if a well fit Bernoulli-Gaussian stochastic process is selected to model the true noise. The incorporation of the Bernoulli-Gaussian channel in the communication system model leads to a better design as corroborated by the computer simulation results presented.</p></div></div></div>

Finding the parameters of a Bernoulli-Gaussian Model

<div><div><div><p>A transmission medium perturbed by an additive noise from which the estimated noise power is all information known, is better modeled as a Gaussian channel. Since the Gaussian channel is, according to Information Theory, the worst channel to transmit information through, this is the most pessimistic assumption. When noise samples are available though, choosing to model the transmission medium using a more sophisticated model pays off. The Bernoulli-Gaussian channel, would be one such a choice. Finding the three parameters that characterize the Bernoulli-Gaussian stochastic process which mathematically models the noise is a task of paramount importance. Many algorithms can be used to estimate the parameters of this model based on numerical methods. In the current work a closed form expression to estimate the model parameters is presented. All that is required besides the estimation of the power of Bernoulli-Gaussian process from the available noise samples is the estimation of two additional quantities: the expected value of the absolute value of the amplitude of the process—the first absolute moment—plus the third absolute moment, viz., the expected value of the third power of the absolute value of the process. An alternative option, often used for power line communication, is to model the transmission medium as a channel in which the noise is represented by a three parameter stochastic process called Middleton Class A. Other models (like generalized-Bernoulli-Gaussian, or Bernoulli- Gaussian with memory) might render a better medium model than the Bernoulli-Gaussian channel. Estimating the parameters of these processes is however a cumbersome task and, as we show in the current work, the rate harvested by using the simple, yet more sophisticated, Bernoulli-Gaussian channel is increased as compared to the, more pessimistic, Gaussian channel, allowing one thus to more closely approach the true capacity. The communication system design can be much improved if a well fit Bernoulli-Gaussian stochastic process is selected to model the true noise. The incorporation of the Bernoulli-Gaussian channel in the communication system model leads to a better design as corroborated by the computer simulation results presented.</p></div></div></div>

Random Unitary Representations of Surface Groups I: Asymptotic Expansions

In this paper, we study random representations of fundamental groups of surfaces into special unitary groups. The random model we use is based on a symplectic form on moduli space due to Atiyah, Bott and Goldman. Let $$\Sigma _{g}$$ Σ g denote a topological surface of genus $$g\ge 2$$ g ≥ 2 . We establish the existence of a large n asymptotic expansion, to any fixed order, for the expected value of the trace of any fixed element of $$\pi _{1}(\Sigma _{g})$$ π 1 ( Σ g ) under a random representation of $$\pi _{1}(\Sigma _{g})$$ π 1 ( Σ g ) into $$\mathsf {SU}(n)$$ SU ( n ) . Each such expected value involves a contribution from all irreducible representations of $$\mathsf {SU}(n)$$ SU ( n ) . The main technical contribution of the paper is effective analytic control of the entire contribution from irreducible representations outside finite sets of carefully chosen rational families of representations.

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

Background Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily included any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.

Posttraumatic Growth after the Fukushima Nuclear Disaster: Examination of Free Descriptions among Fukushima Residents Who Lived in the Evacuation Area

We examined the differences in the posttraumatic growth (PTG) free descriptions from clusters of Fukushima residents (evacuation and non-evacuation zones) who experienced the Great East Japan Earthquake, and the relationship between “recovery from radiation anxiety” and the PTG-free description classification in these regions. A mail survey was conducted in August 2016 among Fukushima residents aged 20–79 years for free descriptions of their PTG. Participants were then divided into the “no anxiety,” “recovered from anxiety,” and “unrecovered from anxiety” groups based on their “recovery from radiation anxiety.” Data from 786 responses were analyzed. The PTG-free descriptions were classified into eight categories. Among those who lived in the evacuation zone versus those in the non-evacuation zone, “relating to others” (non-evacuation zone: 11.9% vs. evacuation zone: 18.4%) and “appreciation of life” (non-evacuation zone: 2.7% vs. evacuation zone: 9.8%) were significantly higher, and “increased awareness of disaster prevention” (non-evacuation zone: 20.4% vs. evacuation zone: 8.0%) was significantly lower. In the evacuation zone, “renewed recognition of nuclear issues” was significantly lower than the expected value in the no anxiety group (3.1%) and significantly higher than the expected value in the recovered group (22.9%). Further studies are needed to build support measures and potentially aid in preparing for future disasters.