Incorporating Expert Opinion Research Articles

Prognosis - subsea oil and gas industry


 
 
 Life extension has been an important and highly discussed issue in nuclear and aviation industries for a long time and it has recently attracted a considerable attention in subsea oil and gas industry. Decision regarding life extension is primarily based on the remaining useful life. The paper explains the technical health and the other factors that influence the remaining useful life. Degradation mechanisms and the lifetime models are discussed, highlighting the limitations of classical approach and the need for Bayesian approach. A model to predict remaining useful life needs to have a capability of handling heterogeneous combination of requirements like degradation modelling, uncertain sensor data handling, and incorporating expert opinion. The paper explores the suitability of using Bayesian Belief Network as a modelling tool for such prediction in subsea oil and gas industry.
 
 

Conceptual and Methodological Challenges in Producing Research Syntheses for Decision-and Policy-Making: An Illustrative Case in Primary Healthcare

This article presents and discusses five challenges encountered in conducting a knowledge synthesis on primary healthcare, commissioned by the Canadian Health Services Research Foundation. These challenges are (1) conceptualizing, defining and operationalizing complex interventions; (2) integrating quantitative and qualitative studies and assessing strength of evidence; (3) incorporating expert opinions and decision-makers’ viewpoints; (4) producing timely results; and (5) presenting the results in a concise yet understandable form. We also propose methods and operational tools to deal with these issues, particularly regarding integration of qualitative and quantitative evidence and incorporation of expert opinions into syntheses. The major challenge of the synthesis was to provide pertinent and useful information for decision- and policy-makers, while maintaining an acceptable level of scientific rigour. This approach seems promising for knowledge syntheses, which sustain a deliberative process that leads to more enlightened decision and policy-making.

Structure of S-3 medical guidelines—Implications for CAM researchers

Medical guidelines are intended to guide decisions and criteria regarding diagnosis, management, and treatment. Strategy is to review, summarize and evaluate the best evidence and most current data on prevention, diagnosis, prognosis and therapy. Then the most important questions related to clinical practice are defined and all possible decision options and their outcomes are identified. There are currently about 800 German guidelines available, classified into 3 levels. Only 50 of them are of the highest, the so-called S-3 level, incorporating expert opinion on the basis of current evidence within the paradigm of evidence-based medicine. To date, Complementary and Alternative Medicine (CAM) is not yet a well integrated part of existing medical guidelines. However, responding to patient request the medical societies react to the change in medical culture, acknowledging CAM and involving CAM experts into the guideline process. The process of developing guidelines involves the identification of key search words and the search for relevant systematic reviews (SR) and publications of randomized controlled trials (RCT) in defined databases. In most cases evaluation is performed according to the 5 grade classification (1–5) of the Oxford Centre of Evidenced Based Medicine. This classification leads to the definition of the 4 grades (A–D) of recommendation. Only SR and/or RCT with an Oxford classification grade 1 can achieve highest recommendation A. During a consensus conference, evaluations and recommendations with their corresponding evidence are discussed. Several guidelines also list the strength of consensus found for each item. It is possible to integrate and incorporate CAM into medical guidelines however study quality is generally low. There are several CAM specific difficulties investigators are facing: e.g. randomization is sometimes difficult, because patients often refuse to accept the conventional treatment in the control group, and for some procedures, like acupuncture or cupping, blinding is impossible. The paper will focus on the general procedures developing guidelines and evidence grades for recommendations and discuss the particular problems of CAM research (blinding, sham procedures) and how the impact of these specificities affects their appearance in the guideline. However, the authors are convinced based on experience in several guidelines, that CAM can be successfully incorporated.

Reasoning about software using metrics and expert opinion

When comparing software programs on the basis of more than one metric a difficulty arises when the metrics are contradictory or if there are no standard acceptance thresholds. An appealing solution in such cases is to incorporate expert opinion to resolve the inconsistencies. A rigorous framework, however, is essential when fusing metrics and expert opinion in this decision-making process. Fortunately, the analytical hierarchy process (AHP) can be used to facilitate rigorous decision-making in this particular problem. In this work a combination of expert opinion and tool-collected measures are used to reason about software programs using AHP. The methodology employed can be adapted to other decision-making problems in software engineering when both metrics data and expert opinion are available, some of which are described.

Evaluating the mid-term competitiveness of a product in a semiconductor fabrication factory with a systematic procedure

Yield is undoubtedly the most critical factor to the competitiveness of a product in a semiconductor fabrication plant. Therefore, evaluating the competitiveness of a product with its yield is a reasonable idea. For this purpose, a systematic procedure is established to evaluate the mid-term competitiveness of a product based on the yield learning model. Further, a new correction function is designed to incorporate expert opinions about the mid-term yield target to main the competitiveness of a product into Chen and Wang’s fuzzy yield learning model. Such expert opinions are very valuable to controlling the yield learning process and have not been considered in traditional models. The modified model ought to be more practical and accurate than the original one. To evaluate the advantages or disadvantages of the proposed methodology, it is applied to the practical data of four products. Experimental results show that the proposed model outperforms the other models by reducing MAPE to only 2%. Besides, as the evaluated competitiveness decreases, the superiority of the proposed model becomes more evident.

A hybrid neural network and selective allowance approach for internal due date assignment in a wafer fabrication plant

A hybrid neural network and selective allowance approach is proposed in this study for internal due date assignment in a wafer fabrication plant (wafer fab). In the first part, a look-ahead self-organization map-fuzzy back propagation network (SOM-FBPN) is constructed to estimate the completion time of a job. Subsequently, a selective allowance policy is established to determine the allowance that will be added to the estimated job completion time. Compared with traditional approaches, the look-ahead SOM-FBPN has many features (including incorporating the future release plan, classifying jobs, and incorporating expert opinions), and the selective allowance policy is novel by considering two different production control issues to avoid missing the due date. According to experimental results, the prediction accuracy of the look-ahead SOM-FBPN was significantly better than those of many existing approaches. Besides, the selective allowance policy considerably improved the due date related performance after adding some extra allowance.

A hybrid look-ahead SOM-FBPN and FIR system for wafer-lot-output time prediction and achievability evaluation

A hybrid system is constructed in this study for wafer-lot-output time prediction and achievability evaluation, which are critical tasks for a wafer fab (fabrication plant). In the first part of the hybrid system, a look-ahead self-organization map fuzzy-back-propagation network (SOM-FBPN) is constructed to predict the output time of a wafer lot. Compared with traditional approaches in this field, the look-ahead SOM-FBPN has three advanced features: incorporating the future release plan, classifying wafer lots, and incorporating expert opinions. According to experimental results, the prediction accuracy and efficiency of the look-ahead SOM-FBPN were significantly better than those of many existing approaches. In the second part of the hybrid system, a set of fuzzy inference rules (FIRs) are established to evaluate the achievability of an output time forecast, which is defined as the possibility that the fabrication on the wafer lot can be finished in time before the output time forecast. Achievability is as important as accuracy and efficiency but has been ignored in traditional studies. With the proposed methodology, both output time prediction and achievability evaluation can be concurrently accomplished.

On using expert opinion in ecological analyses: a frequentist approach

AbstractMany ecological studies are characterized by paucity of hard data. Statistical analysis in such situations leads to flat‐likelihood functions and wide confidence intervals. Although, there is paucity of hard data, expert knowledge about the phenomenon under study is many times available. Such expert opinion may be used to strengthen statistical inference in these situations. Subjective Bayesian is one approach to incorporate expert opinion in statistical studies. This approach, aside from the subjectivity, also faces operational problems. Elicitation of the prior is the most difficult step. Another is the lack of a precise quantitative definition of what characterizes an expert. In this paper, we discuss a different approach to incorporating subjective expert opinion in statistical analyses. We argue that it is easier to elicit data than to elicit a prior. Such elicited data can then be used to supplement the hard, observed data to possibly improve precision of statistical analyses. The approach suggested here also leads to a natural definition of what constitutes a useful expert. We define a useful expert as one whose opinion adds information over and above what is provided by the observed data. This can be quantified in terms of the change in the Fisher information before and after using the expert opinion. One can, thus, avoid the real possibility of using an expert opinion that adds noise, instead of information, to the hard data. We illustrate this approach using an ecological problem of modeling and predicting occurrence of species. An interesting outcome of this analysis is that statistical thinking helps discriminate between a useful expert and a not so useful expert; expertness need not be decided purely on the basis of experience, fame, or such qualitative characteristics. Copyright © 2006 John Wiley & Sons, Ltd.

Incorporating expert opinion into a stochastic model for the chain-ladder technique

To date, much effort has been directed towards the development of stochastic models that are analogous to traditional deterministic methods. In practice, however, the traditional models are often altered to incorporate expert opinion. This paper considers the use of Bayesian models to allow practitioners to apply their judgement to the development factors in the chain-ladder technique. The implementation uses MCMC methods within winBUGS. In this way, it is possible to use stochastic models to obtain predictive distributions of reserves in a much wider range of situations.

A Bayesian Approach to Markov Modelling in Cost-Effectiveness Analyses: Application to Taxane use in Advanced Breast Cancer

SummaryThe paper demonstrates how cost-effectiveness decision analysis may be implemented from a Bayesian perspective, using Markov chain Monte Carlo simulation methods for both the synthesis of relevant evidence input into the model and the evaluation of the model itself. The desirable aspects of a Bayesian approach for this type of analysis include the incorporation of full parameter uncertainty, the ability to perform all the analysis, including each meta-analysis, in a single coherent model and the incorporation of expert opinion either directly or regarding the relative credibility of different data sources. The method is described, and its ease of implementation demonstrated, through a practical example to evaluate the cost-effectiveness of using taxanes for the second-line treatment of advanced breast cancer compared with conventional treatment. For completeness, the results from the Markov chain Monte Carlo simulation model are compared and contrasted with those from a classical Monte Carlo simulation model.

Decision analytical economic modelling within a Bayesian framework: application to prophylactic antibiotics use for caesarean section.

Economic evaluation of health care interventions based on decision analytic modelling can generate valuable information for health policy decision makers. However, the usefulness of the results obtained depends on the quality of the data input into the model; that is, the accuracy of the estimates for the costs, effectiveness, and transition probabilities between the different health states of the model. The aim of this paper is to review the use of Bayesian decision models in economic evaluation and to demonstrate how the individual components required for decision analytical modelling (i.e., systematic review incorporating meta-analyses, estimation of transition probabilities, evaluation of the model, and sensitivity analysis) may be addressed simultaneously in one coherent Bayesian model evaluated using Markov Chain Monte Carlo simulation implemented in the specialist Bayesian statistics software WinBUGS. To illustrate the method described, a simple probabilistic decision model is developed to evaluate the cost implications of using prophylactic antibiotics in caesarean section to reduce the incidence of wound infection. The advantages of using the Bayesian statistical approach outlined compared to the conventional classical approaches to decision analysis include the ability to: (i) perform all necessary analyses, including all intermediate analyses (e.g., meta-analyses) required to derive model parameters, in a single coherent model; (ii) incorporate expert opinion either directly or regarding the relative credibility of different data sources; (iii) use the actual posterior distributions for parameters of interest (opposed to making distributional assumptions necessary for the classical formulation); and (iv) incorporate uncertainty for all model parameters.

ON BAYESIAN ANALYSIS OF BINOMIAL RELIABILITY GROWTH

This paper introduces a new class of prior distributions for reliability growth tests with binomial data under the monotone model. The proposed prior has a conditional form, which accords well with various actual situations in reliability growth tests. The expressions of the corresponding means and variances for all stages with and without conditioning are obtained, and the relationship between the shape of the prior distributions and their parameters are discussed. These results are helpful in order to incorporate expert opinions. The posterior density and the Bayesian lower bound of the relibility at the end of the test, and a computation method for them are given. The new family of prior distributions includes the uniform prior used by Smith (1977) and the ordered Dirichlet priors presented by Mazzuchi and Soyer (1992, 1993) as special cases. Comparisons are made by two examples, which show the limitations of the later two.

Incorporating expert opinion and fine‐scale vegetation mapping into statistical models of faunal distribution

Summary Abiotic environmental predictors and broad‐scale vegetation have been used widely to model the regional distributions of faunal species within forested regions of Australia. These models have been developed using stepwise statistical procedures but incorporate only limited expert involvement of the type sometimes advocated in distribution modelling. The objectives of this study were twofold. First, to evaluate techniques for incorporating fine‐scaled vegetation and growth‐stage mapping into models of species distribution. Secondly, to compare methods that incorporate expert opinion directly into statistical models derived using stepwise statistical procedures. Using faunal data from north‐east New South Wales, Australia, logistic regression models using fine‐scale vegetation and expert opinion were compared with models employing only abiotic and broad vegetation variables. Vegetation and growth‐stage information was incorporated into models of species distribution in two ways, both of which used expert opinion to derive new explanatory variables. The first approach amalgamated fine‐scaled vegetation classes into broader classes of ecological relevance to fauna. In the second approach, ordinal habitat indices were derived from vegetation and growth‐stage mapping using rules specified by an expert panel. These indices described habitat features thought to be relevant to the faunal groups studied (e.g. tree hollow availability, fleshy fruit production). Landscape composition was calculated using these new variables within a 500‐m and 2‐km radius of each site. Each habitat index generated a spatially neutral variable and two spatial context variables. Expert opinion was incorporated during the pre‐modelling, model‐fitting and post‐modelling stages. At the pre‐modelling stage experts developed new explanatory variables based on mapped fine‐scale vegetation and growth‐stage information. At the model‐fitting stage an expert panel selected a subset of potential explanatory variables from the available set. At the post‐modelling stage expert opinion modified or refined maps of predicted species distribution generated by statistical models. For comparative purposes expert opinion was also used to develop maps of species distribution by defining rules within a geographical information system, without the aid of statistical modelling. Predictive accuracy was not improved significantly by incorporating habitat indices derived by applying expert opinion to fine‐scaled vegetation and growth‐stage mapping. Use of expert input at the pre‐modelling stage to derive and select potential explanatory variables therefore does not provide more information than that provided by remotely mapped vegetation. The incorporation of expert opinion at the model‐fitting or post‐modelling stages resulted in small but insignificant gains in predictive accuracy. The predictive accuracy of purely expert models was less than that achieved by approaches based on statistical modelling. The study, one of few available evaluations of expert opinion in models of species distribution, suggests that expert modification of fitted statistical models should be confined to species for which models are grossly in error, or for which insufficient data exist to construct solely statistical models.

Elicited data and incorporation of expert opinion for statistical inference in spatial studies

Spatial data are often sparse by nature. However, in many instances, information may exist in the form of “soft” data, such as expert opinion. Scientists in the field often have a good understanding of the phenomenon under study and may be able to provide valuable information on its likely behavior. It is thus useful to have a sensible mechanism that incorporates expert opinion in inference. The Bayesian paradigm suffers from an inherent subjectivity that is unacceptable to many scientists. Aside from this philosophical problem, elicitation of prior distributions is a difficult task. Moreover, an intentionally misleading expert can have substantial influence on Bayesian inference. In our experience, eliciting data is much more natural to the experts than eliciting prior distributions on the parameters of a probability model that is a purely statistical construct. In this paper we elicit data, i.e., guess values for the realization of the process, from the experts. Utilizing a hierarchical modeling framework, we combine elicited data and actual observed data for inferential purposes. A distinguishing feature of this approach is that even an intentionally misleading expert proves to be useful. Theoretical results and simulations illustrate that incorporating expert opinion via elicited data substantially improves the estimation, prediction, and design aspects of statistical inference for spatial data.

A fuzzy set approach for yield learning modeling in wafer manufacturing

The yield of semiconductor manufacturing can be improved through a learning process. A learning model is usually used to describe the learning process and to predict future yields. However, in traditional learning models such as Gruber's general yield model, the uncertainty and variation inherent in the learning process are not easy to consider. Also there are many strict assumptions about parameter distributions that need to be made. These result in the unreliability and imprecision of yield prediction. To improve the reliability and precision of yield prediction, expert opinions are consulted to evaluate and modify the learning model in this study. The fuzzy set theory is applied to facilitate this consulting process. At first, fuzzy forecasts are generated to predict future yields. The necessity of specifying strict parameter distributions is thus relaxed. Fuzzy yield forecasts can be defuzzified, or their /spl alpha/-cuts can be considered in capacity planning. The interpretation of such a treatment is also intuitive. Then, experts are requested to evaluate the learning model and express their opinions about the parameters in suitable fuzzy numbers or linguistic terms defined in advance. Two correction functions are designed to incorporate expert opinions in the learning model. Some examples are used for demonstration. The advantages of the proposed method are then discussed.

Using a DSS to incorporate expert opinion in strategic product development funding decisions

Research investigating Decision Support Systems (DSS) suggests they provide support to decision makers confronted with unstructured problems. Strategic decisions are often characterized as unstructured: they address external markets, new products, and changing competitors. This paper examines the development and adaptive implementation of a multicriterion DSS, which captured the expertise of recognized experts in different functional groups of an organization, for use in allocating product development funds to new programs. The DSS increased the number of factors considered by decision makers and provided a method for ranking competitors using a discrete set of criteria. The study demonstrates the use of DSS as a vehicle for incorporating of expert opinion in group decision processes, and illustrates specific DSS benefits that include information aggregation and problem structuring.

A study on the use of belief functions for medical expert systems

This paper describes methods for extracting belief functions from data and incorporating expert opinions. The techniques are applied to a medical domain involving the diagnosis of diferent types of liver diseases. This is a domain in which experts are poor at predicting precise (singleton) outcomes. The methodology developed is shown to perform considerably better than the experts alone. Several methods by which to find belief functions from data are compared, and some methods proposed here are shown to outperform a suggestion by Shafer. The system has been fully implemented on a SEQUENT parallel machine and has a user interface which is simple to use and allows for changes and questions.

Bayesian analysis method for incorporating expert opinion and off-site data into rock mechanics studies : Lundquist, R; Fraser, J; Ross-Brown, D Rock Mechanics as a Guide for Efficient Utilization of Natural Resources: Proc 30th U.S. Symposium, Morgantown, 19–22 June 1989P219–226. Publ Rotterdam: A A Balkema, 1989

Bayesian analysis method for incorporating expert opinion and off-site data into rock mechanics studies : Lundquist, R; Fraser, J; Ross-Brown, D Rock Mechanics as a Guide for Efficient Utilization of Natural Resources: Proc 30th U.S. Symposium, Morgantown, 19–22 June 1989P219–226. Publ Rotterdam: A A Balkema, 1989

An Interactive PC-Based Procedure for Reliability Assessment Incorporating Expert Opinion and Survival Data

Abstract In this article I present a new approach for the analysis of life-length data that can be described by a Weibull distribution. The novel feature of my approach pertains to the incorporation of informed judgment or expert opinion into the analysis, using a personal computer in an interactive and user-friendly manner. This approach also provides for incorporating the analyst's opinions on the expertise of the experts. Many of the analyses result in graphical displays, making the development attractive to users not comfortable with analytical expressions.

An Interactive PC-Based Procedure for Reliability Assessment Incorporating Expert Opinion and Survival Data

An Interactive PC-Based Procedure for Reliability Assessment Incorporating Expert Opinion and Survival Data