Expected Value Of Information Research Articles

Systematic review and sequential analysis of Xuebijing Injection in treatment of systemic inflammatory response syndrome

To systematically review the efficacy of Xuebijing Injection combined with western medicine in the treatment of systemic inflammatory response syndrome(SIRS). In this study, CBM, CNKI, Wanfang, VIP, PubMed and EMbase databases were retrieved for clinical randomized controlled trials on the effect of Xuebijing Injection combined with western medicine in the treatment of SIRS from the establishment of the database to July 31, 2020. After screening, Meta-analysis was conducted by RevMan 5.3 software, trial sequential analysis was conducted by TSA 0.9.5.10 beta software, and the evidence quality level was evaluated by GRADEprofiler 3.6.1 software. Meta-analysis showed that Xuebijing Injection combined with western medicine could reduce white blood cell count(MD=-2.32, 95%CI[-2.44,-2.21], P<0.000 01), C-reactive protein count(MD=-22.70, 95%CI[-29.61,-15.79], P<0.000 01), APACHE Ⅱ score(MD=-2.15, 95%CI[-2.43,-1.87], P<0.000 01), tumor necrosis factor alpha count(SMD=-1.23, 95%CI[-1.48,-0.99], P<0.000 01) and interleukin-6 count(SMD=-0.92, 95%CI[-1.15,-0.69], P<0.000 01), improve treatment efficiency(RR=1.39, 95%CI[1.23, 1.56], P<0.000 01), reduce incidence of multiple organ dysfunction(RR=0.47, 95%CI[0.35, 0.64], P<0.000 01) and mortality(RR=0.22, 95%CI[0.13, 0.37], P<0.000 01), which were better than western medicine treatment alone. Trial sequential analysis showed that in terms of reducing the incidence of multiple organ dysfunction and C-reactive protein count, the cumulative Z value passed through the traditional threshold, TSA threshold and expected information value, and reached the required number of cases. GRADE evaluation showed that the level of evidence was low or very low. According to the findings, Xuebijing Injection combined with western medicine is effective in treating SIRS. However, as the low quality of the included studies may affect the reliability of the conclusion, more high-quality studies shall be included for further verification in the future, so as to provide better suggestions for clinical medication.

Information, robustness, and flexibility to manage uncertainties in petroleum field development

Development decisions for petroleum fields are costly and difficult because they involve long-term projects with complex systems and high level of uncertainties. Decision makers may reduce reservoir uncertainties with the acquisition of additional information or protecting the system against uncertainty with flexibility and robustness. Although often preferred, acquiring additional information may be insufficient to mitigate all uncertainties or may be suboptimal when compared to robustness and flexibility. This study proposes a decision framework to estimate the best approach to manage uncertainty at early stages of field development considering the combination of information, robustness, and flexibility. With a predefined set of uncertain scenarios and specialized production strategies optimized for representative models, we can automate the process. We identify the best way to manage uncertainties by assessing system sensitivity and what controls production strategy selection. We improve the traditional estimation of the expected value of information (EVoI), robustness (EVoR), and flexibility (EVoF), by accounting for all changes in the risk curve and weighing the decision maker's attitude. Our proposal was validated in a controlled environment where the reference response is known (a reference model, called UNISIM-I-R, that is not part of the uncertain ensemble, called UNISIM-I-D). The best approach to manage uncertainty in UNISIM-I-D combined features of robust and specialized strategies, resulting in a flexible strategy with robustness. This solution increased oil recovery and the economic return in UNISIM-I-R when compared to taking no action to manage uncertainty, but was suboptimal. These results agree with the rationale behind investing in actions to manage uncertainty (such as flexible and robust strategies), which is to mitigate risks or exploit upsides of uncertainty (i.e., they are intended to prevent or explore extreme outcomes). Investing in such actions is a risky decision in itself because they may not be needed, depending on the features of the real reservoir. Our results in UNISIM-I-D demonstrate the EVoI, EVoR, and EVoF are not additive. Our results in UNISIM-I-R reveal that inherent flexibility in operations (such as in drilling of horizontal wells) may mitigate some risks, reducing the EVoI, EVoR, and EVoF. Our proposal is a good starting point for more quantitative and objective decision-making at the early stages of field development and ultimately prevents discarding attractive solutions based on biases or insufficient metrics. Some improvements may be necessary for EVoI, EVoR, and EVoF estimates depending on the stage of the lifetime of the field, among other factors.

Gradient-Based Optimization for Bayesian Preference Elicitation

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel Monte Carlo method for EVOI optimization, which is much more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k-wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or “partial items,” which are often more cognitively manageable for users. Experiments show that our gradient-based EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.

A semi-quantitative assessment process for improved use of the expected value of information measure in safety management

For safety-related decision problems, investment in reliability information could lead to more informed decision-making. The investment in such information could add value by allowing for reduction and prevention of failures and errors leading to adverse events, depending on whether the additional information is worth the acquisition cost. A common measure used to assess this is the expected value of information (EVOI). However, the traditional EVOI approach has limitations in the way it reflects risks and uncertainties. In particular, the traditional approach of expressing the uncertainties exclusively with probabilities could fail to reflect important aspects of risk related to the strength of the background knowledge on which the probabilities are conditioned (e.g. assumptions and models). An extended, semi-quantitative, approach is suggested, which allows for improved decision-making in the sense that aspects of knowledge strength are included in the decision basis. The article illustrates how the extended approach can improve EVOI assessment in safety management by using an example where there exist an option of acquiring human reliability information prior to a decision between alternative safety educational programs.

Value of information in reservoir development projects: Technical indicators to prioritize uncertainties and information sources

In this study, we consider information acquisition to manage uncertainty in reservoir development projects. Expected Value of Information (EVoI), a long-standing and widely applied technique, is traditionally used to base the decision to acquire additional data. In the field development phase, this calculation can be very complex, a consequence of high uncertainty and multiple decision variables. We propose a methodology to facilitate and reduce the subjectivity of the EVoI analysis, eliminating misconceptions and bias towards particular uncertainties and information sources. We provide indicators to identify, a priori, the uncertainties that can be mitigated with information, on which the EVoI calculation is based. Our workflow considers all available information sources, and integrates the concepts of individual, simultaneous and sequential acquisition of information, and estimates EVoI for perfect and imperfect information. We use a predefined set of candidate production strategies, previously optimized for representative models, which reflect system inputs and outputs. These combined approaches enable automating the EVoI calculation. We applied the methodology to the synthetic reservoir model UNISIM-I-D, a complex case in the development phase. Although there seemed to be great gains with an additional appraisal well, the economic gains from the improved decision did not compensate for delayed production. This supports the importance of performing the EVoI calculation, and not relying on the assumption that more information is always preferable.

Methodology to estimate the value of flexibility under endogenous and exogenous uncertainties

Risk analysis is essential in conceiving petroleum production projects, but it is, many times, underutilized to mitigate risk and increase value. Brazilian offshore fields tend to have high reservoir uncertainties and are heavily dependent on oil prices. Current projects have low flexibility and, in many cases, are ill suited to handle uncertainties. However, flexibilities can be embedded in projects to better prepare for uncertain scenarios. To support the decision process, we must be able to estimate the value of flexibility. The lack of methodologies to do so using reservoir simulations is the main motivation for this work. This paper proposes a five-stage methodology to estimate the value of flexibility in petroleum production projects under endogenous and exogenous uncertainties. Each stage is split into steps to identify the details of problem modeling and strategy selection. The EVoF (expected value of the flexibility) is a probabilistic concept similar to EVoI (expected value of information) based on projections and calculated prior to starting a project. The use of reservoir simulation models considering flexibility to predict production improves estimates of EVoF. High-fidelity simulation models capture flow and geological characterization in porous media, and representative models evaluate uncertainties. Economic uncertainty is represented over time using geometric Brownian motion. The overall process integrates the complex representation of attributes from different sources with a dynamic approach to allow the implementation of flexibility over time. This new integrated approach focuses on identifying drivers that evidence the need for flexible projects and investigates flexibilities to face uncertainties while observing their impact on results. Therefore, decision makers are able to compare rigid and flexible projects based on reliable predictions. The study case highlights various aspects related to the conception of flexible projects, such as proper choice of design variables and identification of the potential of the flexibility, and it shows that the methodology is suitable for a decision-making process that integrates simulation modeling and uncertainty analysis. We estimate EVoF from the distribution of NPV differences and show the increase of EVoF as design variables and decision rules change and optimized strategies for representative models are applied.

Shannon information entropy in the canonical genetic code

The Shannon entropy measures the expected information value of messages. As with thermodynamic entropy, the Shannon entropy is only defined within a system that identifies at the outset the collections of possible messages, analogous to microstates, that will be considered indistinguishable macrostates. This fundamental insight is applied here for the first time to amino acid alphabets, which group the twenty common amino acids into families based on chemical and physical similarities. To evaluate these schemas objectively, a novel quantitative method is introduced based the inherent redundancy in the canonical genetic code. Each alphabet is taken as a separate system that partitions the 64 possible RNA codons, the microstates, into families, the macrostates. By calculating the normalized mutual information, which measures the reduction in Shannon entropy, conveyed by single nucleotide messages, groupings that best leverage this aspect of fault tolerance in the code are identified. The relative importance of properties related to protein folding - like hydropathy and size - and function, including side-chain acidity, can also be estimated. This approach allows the quantification of the average information value of nucleotide positions, which can shed light on the coevolution of the canonical genetic code with the tRNA-protein translation mechanism.

Cost-effectiveness and affordability of community mobilisation through women's groups and quality improvement in health facilities (MaiKhanda trial) in Malawi.

BackgroundUnderstanding the cost-effectiveness and affordability of interventions to reduce maternal and newborn deaths is critical to persuading policymakers and donors to implement at scale. The effectiveness of community mobilisation through women’s groups and health facility quality improvement, both aiming to reduce maternal and neonatal mortality, was assessed by a cluster randomised controlled trial conducted in rural Malawi in 2008–2010. In this paper, we calculate intervention cost-effectiveness and model the affordability of the interventions at scale.MethodsBayesian methods are used to estimate the incremental cost-effectiveness of the community and facility interventions on their own (CI, FI), and together (FICI), compared to current practice in rural Malawi. Effects are estimated with Monte Carlo simulation using the combined full probability distributions of intervention effects on stillbirths, neonatal deaths and maternal deaths. Cost data was collected prospectively from a provider perspective using an ingredients approach and disaggregated at the intervention (not cluster or individual) level. Expected Incremental Benefit, Cost-effectiveness Acceptability Curves and Expected Value of Information (EVI) were calculated using a threshold of $780 per disability-adjusted life-year (DALY) averted, the per capita gross domestic product of Malawi in 2013 international $.ResultsThe incremental cost-effectiveness of CI, FI, and combined FICI was $79, $281, and $146 per DALY averted respectively, compared to current practice. FI is dominated by CI and FICI. Taking into account uncertainty, both CI and combined FICI are highly likely to be cost effective (probability 98% and 93%, EVI $210,423 and $598,177 respectively). Combined FICI is incrementally cost effective compared to either intervention individually (probability 60%, ICER $292, EIB $9,334,580 compared to CI). Future scenarios also found FICI to be the optimal decision. Scaling-up to the whole of Malawi, CI is of greatest value for money, potentially averting 13.0% of remaining annual DALYs from stillbirths, neonatal and maternal deaths for the equivalent of 6.8% of current annual expenditure on maternal and neonatal health in Malawi.ConclusionsCommunity mobilisation through women’s groups is a highly cost-effective and affordable strategy to reduce maternal and neonatal mortality in Malawi. Combining community mobilisation with health facility quality improvement is more effective, more costly, but also highly cost-effective and potentially affordable in this context.Electronic supplementary materialThe online version of this article (doi:10.1186/s12962-014-0028-2) contains supplementary material, which is available to authorized users.

Intravenous immunoglobulin for severe sepsis and septic shock: clinical effectiveness, cost-effectiveness and value of a further randomised controlled trial.

IntroductionPrior to investing in a large, multicentre randomised controlled trial (RCT), the National Institute for Health Research in the UK called for an evaluation of the feasibility and value for money of undertaking a trial on intravenous immunoglobulin (IVIG) as an adjuvant therapy for severe sepsis/septic shock.MethodsIn response to this call, this study assessed the clinical and cost-effectiveness of IVIG (using a decision model), and evaluated the value of conducting an RCT (using expected value of information (EVI) analysis). The evidence informing such assessments was obtained through a series of systematic reviews and meta-analyses. Further primary data analyses were also undertaken using the Intensive Care National Audit & Research Centre Case Mix Programme Database, and a Scottish Intensive Care Society research study.ResultsWe found a large degree of statistical heterogeneity in the clinical evidence on treatment effect, and the source of such heterogeneity was unclear. The incremental cost-effectiveness ratio of IVIG is within the borderline region of estimates considered to represent value for money, but results appear highly sensitive to the choice of model used for clinical effectiveness. This was also the case with EVI estimates, with maximum payoffs from conducting a further clinical trial between £137 and £1,011 million.ConclusionsOur analyses suggest that there is a need for a further RCT. Results on the value of conducting such research, however, were sensitive to the clinical effectiveness model used, reflecting the high level of heterogeneity in the evidence base.

Network vs. Pairwise Meta-Analyses: A Case Study of the Impact of an Evidence-Synthesis Paradigm on Value of Information Outcomes

To evaluate the impact of using two evidence-synthesis paradigms, pairwise meta-analysis (PMA) vs. network meta-analysis (NMA), on the expected value of information (EVI) outcomes, using pharmacotherapy of chronic obstructive pulmonary disease as a case study. Bayesian random-effects PMAs were performed for each pharmacotherapy vs. placebo, and a Bayesian random-effects NMA was performed combining both placebo-controlled and head-to-head trials. Both provided comparative rate ratio (RR) estimates between each pharmacotherapy vs. placebo. A Markov model was developed to project costs and quality-adjusted life-years of five commonly used treatments for chronic obsructive pulmonary disorder. RRs for the treatment effect compared with placebo derived using PMA and NMA were used alongside values from the literature to populate the model. In addition to standard cost-effectiveness outputs, we calculated and compared the expected value of perfect information (EVPI) and the expected value of partial perfect information (EVPPI) for treatment effects, for comparisons that included all or a subset of treatments. The network of evidence included five different treatments, compared in 19 randomized controlled trials (RCTs), which in total included 28,172 individuals. The cost-effectiveness outcomes were similar between the two evidence-synthesis paradigms. The individual EVPI for all treatments was Can$1,262 for PMA-based analyses and Can$572 for NMA-based analyses. For all comparisons involving two, three, or four treatments, the comparison with the highest EVPI was different between the two methods. Similarly, the choice of PMA or NMA had resulted in substantially different EVPPI rankings. Our case study shows that the choice of PMA or NMA can have significant effects on the EVI results. Under comparable conditions, the incorporation of more evidence in the NMA most likely increases the precision of estimates and therefore is likely to result in lower EVI outcomes. As our study demonstrates, the difference in EVI outcomes can be substantial, potentially affecting the decision to conduct research and the design of future research.

ARE VALUE OF INFORMATION METHODS READY FOR PRIME TIME? AN APPLICATION TO ALTERNATIVE TREATMENT STRATEGIES FOR NSTEMI PATIENTS

The use of value of information methods to inform trial design has been widely advocated but there have been few empirical applications of these methods and there is little evidence they are widely used in decision making. This study considers the usefulness of value of information models in the context of a real clinical decision problem relating to alternative diagnostic strategies for patients with a recent non-ST elevated myocardial infarction. A pretrial economic model is constructed to consider the cost-effectiveness of two competing strategies: coronary angiography alone or in conjunction with fractional flow reserve measurement. A closed-form solution to the expected benefits of information is used with optimal sample size estimated for a range of models reflecting increasingly realistic assumptions and alternative decision contexts. Fractional flow reserve measurement is expected to be cost-effective with an incremental cost-effectiveness ratio of GBP 1,621, however, there is considerable uncertainty in this estimate and consequently a large expected value to reducing this uncertainty via a trial. The recommended sample size is strongly affected by the reality of the assumptions of the expected value of information (EVI) model and the decision context. Value of information models can provide a simple and flexible approach to clinical trial design and are more consistent with the constraints and objectives of the healthcare system than traditional frequentist approaches. However, the variation in sample size estimates demonstrates that it is essential that appropriate model parameters and decision contexts are used in their application.

Decision-theoretic reflections on processing a fingermark

A recent publication in this journal [1] presented the results of a field study that revealed the data provided by the fingermarks not processed in a forensic science laboratory. In their study, the authors were interested in the usefulness of this additional data in order to determine whether such fingermarks would have been worth submitting to the fingermark processing workflow. Taking these ideas as a starting point, this communication here places the fingermark in its context of a case brought before a court, and examines the question of processing or not processing a fingermark from a decision-theoretic point of view. The decision-theoretic framework presented provides an answer to this question in the form of a quantified expression of the expected value of information (EVOI) associated with the processed fingermark, which can then be compared with the cost of processing the mark.

Value of information and mobility constraints for sampling with mobile sensors

Wireless sensor networks (WSNs) play a vital role in environmental monitoring. Advances in mobile sensors offer new opportunities to improve phenomenon predictions by adapting spatial sampling to local variability. Two issues are relevant: which location should be sampled and which mobile sensor should move to do it? This paper proposes a form of adaptive sampling by mobile sensors according to the expected value of information (EVoI) and mobility constraints. EVoI allows decisions to be made about the location to observe. It minimises the expected costs of wrong predictions about a phenomenon using a spatially aggregated EVoI criterion. Mobility constraints allow decisions to be made about which sensor to move. A cost-distance criterion is used to minimise unwanted effects of sensor mobility on the WSN itself, such as energy depletion. We implemented our approach using a synthetic data set, representing a typical monitoring scenario with heterogeneous mobile sensors. To assess the method, it was compared with a random selection of sample locations. The results demonstrate that EVoI enables selecting the most informative locations, while mobility constraints provide the needed context for sensor selection. This paper therefore provides insights about how sensor mobility can be efficiently managed to improve knowledge about a monitored phenomenon.

An evaluation of the feasibility, cost and value of information of a multicentre randomised controlled trial of intravenous immunoglobulin for sepsis (severe sepsis and septic shock): incorporating a systematic review, meta-analysis and value of information analysis.

Sepsis is a syndrome characterised by a systemic inflammatory response to infection that leads to rapid acute organ failure and potentially rapid decline to death. Intravenous immunoglobulin (IVIG), a blood product derived from human donor blood, has been proposed as an adjuvant therapy for sepsis. To describe current practice in the management of adult patients severely ill with sepsis (severe sepsis or septic shock) in the UK; to assess the clinical effectiveness of IVIG for severe sepsis and septic shock and to obtain the appropriate inputs for the relative efficacy parameters, and the key uncertainties associated with these parameters, required to populate the decision model; to develop a decision-analytic model structure and identify key parameter inputs consistent with the decision problem and relevant to an NHS setting; and to populate the decision model and determine the cost-effectiveness of IVIG and to estimate the value of additional primary research. Existing literature on IVIG and severe sepsis. Existing case-mix and outcome data on critical care admissions. Survey data on management of admissions with severe sepsis. Databases searched for clinical effectiveness were Cochrane Infectious Diseases Group Specialized Trials Register, the Cochrane Trials Register, MEDLINE and EMBASE. Dates searched were 1 January 2002 to 2 October 2009 to update previous Cochrane review. Databases searched for cost-effectiveness were NHS Economic Evaluation Database (NHS EED) to 2 October 2009, MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations and EMBASE to 20 October 2009. Systematic literature searching with data extraction, descriptive analysis and clinical effectiveness and cost-effectiveness modelling of IVIG in severe sepsis. Additional primary data analysis. Expected value of information (EVI) analysis. Our meta-analysis, the first to simultaneously allow for type of IVIG (IVIG or immunoglobulin M-enriched polyclonal IVIG), choice of control (no treatment or albumin), study quality/publication bias and other potential covariates, indicated that the treatment effect of IVIG on mortality for patients with severe sepsis is borderline significant with a large degree of heterogeneity in treatment effect between individual studies. Modelling indicated that there were issues with bias associated with trial methodology, publication and small-study effects with the current evidence. The large degree of heterogeneity in treatment effects between studies, however, could be explained (best-fitting model) by a measure of study quality (i.e. use of albumin as control - as an indicator of proper blinding to treatment as a proxy for study quality - associated with decreased effect) and duration of IVIG therapy (longer duration associated with increased effect). In-depth discussion within the Expert Group on duration of IVIG therapy, with daily dose and total dose also clearly inter-related, indicated no clear clinical rationale for this association and exposed a lack of evidence on the understanding of the mechanism of action of IVIG in severe sepsis. Although the EVI analyses suggested substantial expected net benefit from a large, multicentre randomised controlled trial (RCT) evaluating the clinical effectiveness of IVIG, the remaining uncertainties around the design of such a study mean that we are unable to recommend it at this time. As has been identified in previous meta-analyses, there are issues with the methodological quality of the available evidence. Although the results highlight the value for money obtained in conducting further primary research in this area, the biggest limitation for such research regards the uncertainties over the mechanism of action of IVIG and the heterogeneous nature of severe sepsis. Resolving these would allow for better definition of the plausibility of the effectiveness scenarios presented and, consequently, a better understanding of the cost-effectiveness of this treatment. This information would also inform the design of future, primary evaluative research. Our recommendations for future research focus on filling the knowledge gaps to inform a future multicentre RCT prior to recommending its immediate design and conduct. The National Institute for Health Research Health Technology Assessment programme.

Classification of Objects on the Base of the Expected Information Value

Classification of Objects on the Base of the Expected Information Value

Sequential Sampling to Myopically Maximize the Expected Value of Information

Statistical selection procedures are used to select the best of a finite set of alternatives, where “best” is defined in terms of each alternative's unknown expected value, and the expected values are inferred through statistical sampling. One effective approach, which is based on a Bayesian probability model for the unknown mean performance of each alternative, allocates samples based on maximizing an approximation to the expected value of information (EVI) from those samples. The approximations include asymptotic and probabilistic approximations. This paper derives sampling allocations that avoid most of those approximations to the EVI but entails sequential myopic sampling from a single alternative per stage of sampling. We demonstrate empirically that the benefits of reducing the number of approximations in the previous algorithms are typically outweighed by the deleterious effects of a sequential one-step myopic allocation when more than a few dozen samples are allocated. Theory clarifies the derivation of selection procedures that are based on the EVI.

Exploring the Research Decision Space: The Expected Value of Information for Sequential Research Designs

To investigate the expected value of partial perfect information (EVPPI) and the research decisions it can address. Expected value of information (EVI) analysis assesses the expected gain in net benefit from further research. Where the expected value of perfect information (EVPI) exceeds the costs of additional research, EVPPI can be used to identify parameters that contribute most to the EVPI and parameters with no EVPPI that may be disregarded as targets for further research. Recently, it was noted that parameters with low EVPPI for a one-off may be associated with high EVPPI when considered as part of a sequential design. This article examines the characteristics and role of conditional and sequential EVPPI in EVI analysis. The calculation of EVPPI is demonstrated for single parameters, groups of parameters, and conditional and sequential EVPPI. Conditional EVPPI is the value of perfect information about one parameter, conditional on having obtained perfect information about another. Sequential EVPPI is the value of perfect information for a sequential research design to investigate first one parameter, then another. Conditional EVPPI differs from the individual EVPPI for a single parameter. Sequential EVPPI includes elements from the joint EVPPI for the parameters and the EVPPI for the first parameter in sequence. Sequential designs allow abandonment of research on the second parameter on the basis of additional information obtained on the first. The research decision space addressed by EVI analyses can be widened by incorporating sequential EVPPI to assess sequential research designs.

Main‐path analysis and path‐dependent transitions in HistCite™‐based historiograms

AbstractWith the program HistCite™ it is possible to generate and visualize the most relevant papers in a set of documents retrieved from the Science Citation Index. Historical reconstructions of scientific developments can be represented chronologically as developments in networks of citation relations extracted from scientific literature. This study aims to go beyond the historical reconstruction of scientific knowledge, enriching the output of HistCite™ with algorithms from social‐network analysis and information theory. Using main‐path analysis, it is possible to highlight the structural backbone in the development of a scientific field. The expected information value of the message can be used to indicate whether change in the distribution (of citations) has occurred to such an extent that a path‐dependency is generated. This provides us with a measure of evolutionary change between subsequent documents. The “forgetting and rewriting” of historically prior events at the research front can thus be indicated. These three methods—HistCite, main path and path dependent transitions—are applied to a set of documents related to fullerenes and the fullerene‐like structures known as nanotubes.

A Forensic Evaluation of the National Emphysema Treatment Trial Using the Expected Value of Information Approach

Expected value of information (EVI) analyses allow researchers to estimate the returns to conducting research. We used EVI techniques to estimate the value of the National Emphysema Treatment Trial (NETT), a multicenter randomized trial of lung-volume-reduction surgery (LVRS) versus medical therapy (MT) for patients with severe emphysema, then compared that result to the trial cost. We gathered information on costs and benefits of LVRS and MT before the trial and the costs of conducting the NETT, and compared these data with the results of the cost-effectiveness analysis conducted alongside the trial. We used 2 thresholds to represent the societal value of a quality-adjusted life year (QALY): USD 50,000 and USD100,000. The cost effectiveness of LVRS versus MT using historical (nontrial) information was USD 305,000/QALY. Based on these data and the threshold incremental cost-effectiveness ratio values, the expected value of perfect information was USD 46 million and USD 670 million for thresholds USD 50,000 and USD 100,000 per QALY, respectively. The NETT was powered for 1,250 patients in each arm; ultimately approximately 600 patients in each arm were recruited. With 1,250 patients per arm, the expected value of sample information was USD 660 million for the threshold of USD100,000. The actual cost of the NETT was approximately USD 60 million. The expected net benefit of sampling was USD 600 million. Given the difference between the cost of the trial and the economic benefits of the information, the EVI analyses suggest that federal investment in the NETT trial represented good value for money.

The Half-Life of Truth: What Are Appropriate Time Horizons for Research Decisions?

To evaluate alternative approaches taken to estimate the population that could benefit from research and to demonstrate that explicitly modeling future change leads to more appropriate estimates of the expected value of information (EVI). Existing approaches to estimating the population typically focus on the time horizon for decisions, employing seemingly arbitrary estimates of the appropriate horizon. These approaches implicitly use the time horizon as a proxy for future changes in technologies, prices, and information. Different approaches to quantifying the time horizon are explored, in the context of a stylized model, to demonstrate the impact of uncertainty in this estimate on EVI. An alternative approach is developed that explicitly models future changes in technologies, prices, and information and that demonstrates the impact on EVI estimates. Explicitly modeling future changes means that the EVI for the decision problem may increase or decrease over time, but the EVI for the group of parameters that can be evaluated by current research tends to decline. The finite and infinite time horizons for the decision problem represent special cases (e.g., price shock or no changes, respectively). This type of analysis can be used to inform policy decisions relating to the timing of research. The value of information depends on future changes in technologies, prices, and evidence. Finite time horizons for decision problems can be seen as a proxy for the complex and uncertain process of future change. A more explicit approach to modeling these changes could provide a more appropriate basis for calculating EVI, but this raises a number of significant methodological and technical challenges.