The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Benjamin Djulbegovic - One of the best experts on this subject based on the ideXlab platform.
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when are randomized trials unnecessary a signal Detection Theory approach to approving new treatments based on non randomized studies
Journal of Evaluation in Clinical Practice, 2020Co-Authors: Benjamin Djulbegovic, Marianne Razavi, Iztok HozoAbstract:RATIONALE, AIMS AND OBJECTIVES New therapies are increasingly approved by regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) based on testing in non-randomized clinical trials. These treatments have typically displayed "dramatic effects" (ie, effects that are considered large enough to obviate the combined effects of biases and random errors that may affect the study results). The agencies, however, have not identified how large these effects should be to avoid the need for further testing in randomized controlled trials (RCTs). We investigated the effect size that would circumvent the need for further RCTs testing by the regulatory agencies. We hypothesized that the approval of therapeutic interventions by regulators is based on heuristic decision making whose accuracy can be best characterized by the application of signal Detection Theory (SDT). METHODS We merged the EMA and FDA database of approvals based on non-RCT comparisons. We excluded duplicate entries between the two databases. We included a total of 134 approvals of drugs and devices based on non-RCTs. We integrated Weber-Fechner law of psychophysics and recognition heuristics within SDT to provide descriptive explanations of the decisions made by the FDA and EMA to approve new treatments based on non-randomized studies without requiring further testing in RCTs. RESULTS Our findings suggest that when the difference between novel treatments and the historical control is at least one logarithm (base 10) of magnitude, the veracity of testing in non-RCTs seems to be established. CONCLUSION Drug developers and practitioners alike can use the change in one logarithm of effect size as a benchmark to decide if further testing in RCTs should be pursued, or as a guide to interpreting the results reported in non-randomized studies. However, further research would be useful to better characterize the threshold of effect size above which testing in RCTs is not needed.
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towards Theory integration threshold model as a link between signal Detection Theory fast and frugal trees and evidence accumulation Theory
Journal of Evaluation in Clinical Practice, 2017Co-Authors: Iztok Hozo, Benjamin Djulbegovic, Athanasios Tsalatsanis, Shenghua Luan, Gerd GigerenzerAbstract:Rationale, aims and objectives Theories of decision making are divided between those aiming to help decision makers in the real, ‘large’ world and those who study decisions in idealized ‘small’ world settings. For the most part, these large- and small-world decision theories remain disconnected. Methods We linked the small-world decision theoretic concepts of signal Detection Theory (SDT) and evidence accumulation Theory (EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees (FFT). Results We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information – the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. Conclusions By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making (FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision Theory paradigms.
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towards Theory integration threshold model as a link between signal Detection Theory fast and frugal trees and evidence accumulation Theory
Journal of Evaluation in Clinical Practice, 2017Co-Authors: Iztok Hozo, Benjamin Djulbegovic, Athanasios Tsalatsanis, Shenghua Luan, Gerd GigerenzerAbstract:Rationale, aims and objectives Theories of decision making are divided between those aiming to help decision makers in the real, ‘large’ world and those who study decisions in idealized ‘small’ world settings. For the most part, these large- and small-world decision theories remain disconnected. Methods We linked the small-world decision theoretic concepts of signal Detection Theory (SDT) and evidence accumulation Theory (EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees (FFT). Results We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information – the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. Conclusions By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making (FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision Theory paradigms.
Lawrence T. Decarlo - One of the best experts on this subject based on the ideXlab platform.
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an item response model for true false exams based on signal Detection Theory
Applied Psychological Measurement, 2020Co-Authors: Lawrence T. DecarloAbstract:A true–false exam can be viewed as being a signal Detection task—the task is to detect whether or not an item is true (signal) or false (noise). In terms of signal Detection Theory (SDT), examinees...
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a model of rater behavior in essay grading based on signal Detection Theory
Journal of Educational Measurement, 2005Co-Authors: Lawrence T. DecarloAbstract:An approach to essay grading based on signal Detection Theory (SDT) is presented. SDT offers a basis for understanding rater behavior with respect to the scoring of construct responses, in that it provides a Theory of psychological processes underlying the raters’ behavior. The approach also provides measures of the precision of the raters and the accuracy of classifications. An application of latent class SDT to essay grading is detailed, and similarities to and differences from item response Theory (IRT) are noted. The validity and utility of classifications obtained from the SDT model and scores obtained from IRT models are compared. Validity coefficients were found to be about equal in magnitude across SDT and IRT models. Results from a simulation study of a 5-class SDT model with eight raters are also presented.
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an application of signal Detection Theory with finite mixture distributions to source discrimination
Journal of Experimental Psychology: Learning Memory and Cognition, 2003Co-Authors: Lawrence T. DecarloAbstract:A mixture extension of signal Detection Theory is applied to source discrimination. The basic idea of the approach is that only a portion of the sources (say A or B) of items to be discriminated is encoded or attended to during the study period. As a result, in addition to 2 underlying probability distributions associated with the 2 sources, there is a 3rd distribution that represents items for which sources were not attended to. Thus, over trials, the observed response results from a mixture of an attended (A or B) distribution and a nonattended distribution. The situation differs in an interesting way from Detection in that, for Detection, there is mixing only on signal trials and not on noise trials, whereas for discrimination, there is mixing on both A and B trials. Predictions of the mixture model are examined for data from several recent studies and in a new experiment.
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source monitoring and multivariate signal Detection Theory with a model for selection
Journal of Mathematical Psychology, 2003Co-Authors: Lawrence T. DecarloAbstract:Participants in source monitoring studies,in addition to determining whether an item is old or new,also discriminate the source of the item,such as whether the item was presented in a male or female voice. This article shows how to apply multivariate signal Detection Theory (SDT) to source monitoring. An interesting aspect of one version of the source monitoring procedure,from the perspective of multivariate SDT,is that it involves a type of selection,in that a discrimination response is observed only if the Detection decision is that an item is old. If the selection is ignored,then the estimate of the discrimination parameter can be biased; the nature and magnitude of the bias are illustrated. A bivariate signal Detection model that recognizes selection is presented and its application is illustrated. The approach to source monitoring via multivariate SDT provides new results that are informative about underlying psychological processes. r 2003 Elsevier Science (USA). All rights reserved.
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signal Detection Theory with finite mixture distributions theoretical developments with applications to recognition memory
Psychological Review, 2002Co-Authors: Lawrence T. DecarloAbstract:An extension of signal Detection Theory (SDT) that incorporates mixtures of the underlying distributions is presented. The mixtures can be motivated by the idea that a presentation of a signal shifts the location of an underlying distribution only if the observer is attending to the signal; otherwise, the distribution is not shifted or is only partially shifted. Thus, trials with a signal presentation consist of a mixture of 2 (or more) latent classes of trials. Mixture SDT provides a general theoretical framework that offers a new perspective on a number of findings. For example, mixture SDT offers an alternative to the unequal variance signal Detection model; it can also account for nonlinear normal receiver operating characteristic curves, as found in recent research. Signal Detection Theory (SDT) provides a theoretical framework that has been quite useful in psychology and other fields (see Gescheider, 1997; Macmillan & Creelman, 1991; Swets, 1996). A basic idea of SDT is that decisions about the presence or absence of an event are based on decision criteria and on perceptions of the event or nonevent, with the perceptions being represented by probability distributions on an underlying continuum. Thus, in its simplest form, the Theory considers two basic aspects of Detection—the underlying representations, which are interpreted as psychological distributions of some sort (e.g., of perception or familiarity), and a decision aspect, which involves the use of decision criteria to arrive at a response. The present article extends SDT by viewing Detection as consisting of an additional process. The result is a simple and psychologically meaningful extension of SDT that can be applied to any area of research where SDT has been applied. The approach is illustrated with applications to research on recognition memory, where the additional process can be interpreted as attention. In
Iztok Hozo - One of the best experts on this subject based on the ideXlab platform.
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when are randomized trials unnecessary a signal Detection Theory approach to approving new treatments based on non randomized studies
Journal of Evaluation in Clinical Practice, 2020Co-Authors: Benjamin Djulbegovic, Marianne Razavi, Iztok HozoAbstract:RATIONALE, AIMS AND OBJECTIVES New therapies are increasingly approved by regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) based on testing in non-randomized clinical trials. These treatments have typically displayed "dramatic effects" (ie, effects that are considered large enough to obviate the combined effects of biases and random errors that may affect the study results). The agencies, however, have not identified how large these effects should be to avoid the need for further testing in randomized controlled trials (RCTs). We investigated the effect size that would circumvent the need for further RCTs testing by the regulatory agencies. We hypothesized that the approval of therapeutic interventions by regulators is based on heuristic decision making whose accuracy can be best characterized by the application of signal Detection Theory (SDT). METHODS We merged the EMA and FDA database of approvals based on non-RCT comparisons. We excluded duplicate entries between the two databases. We included a total of 134 approvals of drugs and devices based on non-RCTs. We integrated Weber-Fechner law of psychophysics and recognition heuristics within SDT to provide descriptive explanations of the decisions made by the FDA and EMA to approve new treatments based on non-randomized studies without requiring further testing in RCTs. RESULTS Our findings suggest that when the difference between novel treatments and the historical control is at least one logarithm (base 10) of magnitude, the veracity of testing in non-RCTs seems to be established. CONCLUSION Drug developers and practitioners alike can use the change in one logarithm of effect size as a benchmark to decide if further testing in RCTs should be pursued, or as a guide to interpreting the results reported in non-randomized studies. However, further research would be useful to better characterize the threshold of effect size above which testing in RCTs is not needed.
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towards Theory integration threshold model as a link between signal Detection Theory fast and frugal trees and evidence accumulation Theory
Journal of Evaluation in Clinical Practice, 2017Co-Authors: Iztok Hozo, Benjamin Djulbegovic, Athanasios Tsalatsanis, Shenghua Luan, Gerd GigerenzerAbstract:Rationale, aims and objectives Theories of decision making are divided between those aiming to help decision makers in the real, ‘large’ world and those who study decisions in idealized ‘small’ world settings. For the most part, these large- and small-world decision theories remain disconnected. Methods We linked the small-world decision theoretic concepts of signal Detection Theory (SDT) and evidence accumulation Theory (EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees (FFT). Results We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information – the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. Conclusions By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making (FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision Theory paradigms.
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towards Theory integration threshold model as a link between signal Detection Theory fast and frugal trees and evidence accumulation Theory
Journal of Evaluation in Clinical Practice, 2017Co-Authors: Iztok Hozo, Benjamin Djulbegovic, Athanasios Tsalatsanis, Shenghua Luan, Gerd GigerenzerAbstract:Rationale, aims and objectives Theories of decision making are divided between those aiming to help decision makers in the real, ‘large’ world and those who study decisions in idealized ‘small’ world settings. For the most part, these large- and small-world decision theories remain disconnected. Methods We linked the small-world decision theoretic concepts of signal Detection Theory (SDT) and evidence accumulation Theory (EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees (FFT). Results We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information – the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. Conclusions By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making (FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision Theory paradigms.
Gerd Gigerenzer - One of the best experts on this subject based on the ideXlab platform.
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towards Theory integration threshold model as a link between signal Detection Theory fast and frugal trees and evidence accumulation Theory
Journal of Evaluation in Clinical Practice, 2017Co-Authors: Iztok Hozo, Benjamin Djulbegovic, Athanasios Tsalatsanis, Shenghua Luan, Gerd GigerenzerAbstract:Rationale, aims and objectives Theories of decision making are divided between those aiming to help decision makers in the real, ‘large’ world and those who study decisions in idealized ‘small’ world settings. For the most part, these large- and small-world decision theories remain disconnected. Methods We linked the small-world decision theoretic concepts of signal Detection Theory (SDT) and evidence accumulation Theory (EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees (FFT). Results We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information – the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. Conclusions By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making (FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision Theory paradigms.
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towards Theory integration threshold model as a link between signal Detection Theory fast and frugal trees and evidence accumulation Theory
Journal of Evaluation in Clinical Practice, 2017Co-Authors: Iztok Hozo, Benjamin Djulbegovic, Athanasios Tsalatsanis, Shenghua Luan, Gerd GigerenzerAbstract:Rationale, aims and objectives Theories of decision making are divided between those aiming to help decision makers in the real, ‘large’ world and those who study decisions in idealized ‘small’ world settings. For the most part, these large- and small-world decision theories remain disconnected. Methods We linked the small-world decision theoretic concepts of signal Detection Theory (SDT) and evidence accumulation Theory (EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees (FFT). Results We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information – the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. Conclusions By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making (FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision Theory paradigms.
Arturo Molina - One of the best experts on this subject based on the ideXlab platform.
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usability perceptions and beliefs about smart thermostats by chi square test signal Detection Theory and fuzzy Detection Theory in regions of mexico
Frontiers in energy, 2019Co-Authors: Pedro Ponce, Therese Peffer, Arturo MolinaAbstract:It is well known that smart thermostats (STs) have become key devices in the implementation of smart homes; thus, they are considered as primary elements for the control of electrical energy consumption in households. Moreover, energy consumption is drastically affected when the end users select unsuitable STs or when they do not use the STs correctly. Furthermore, in future, Mexico will face serious electrical energy challenges that can be considerably resolved if the end users operate the STs in a correct manner. Hence, it is important to carry out an in-depth study and analysis on thermostats, by focusing on social aspects that influence the technological use and performance of the thermostats. This paper proposes the use of a signal Detection Theory (SDT), fuzzy Detection Theory (FDT), and chi-square (CS) test in order to understand the perceptions and beliefs of end users about the use of STs in Mexico. This paper extensively shows the perceptions and beliefs about the selected thermostats in Mexico. Besides, it presents an in-depth discussion on the cognitive perceptions and beliefs of end users. Moreover, it shows why the expectations of the end users about STs are not met. It also promotes the technological and social development of STs such that they are relatively more accepted in complex electrical grids such as smart grids.
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End user perceptions toward smart grid technology: Acceptance, adoption, risks, and trust
Renewable & Sustainable Energy Reviews, 2016Co-Authors: Pedro Ponce, Kenneth Polasko, Arturo MolinaAbstract:Although smart grid technology has been extensively accepted, social factors influence the performance of smart grid systems. This smart technology will enable the automated monitoring and control of the power delivery system, increase the capacity of the power delivery system, and enhance the performance and connectivity of end users. However, the perceptions of end users are a key factor for adoption of this technology. When end users do not fully accept the smart grid, the operation of the smart grid is not satisfactory. Most literature has concentrated on the technological aspects of smart grids; a technological solution may be defined as one that requires a change only in the developed technology, demanding little or no change in human values or ideas of morality about the usage of electrical energy. However, the solutions to the problems of implementing smart grid technology are not to be found only in technological aspects. This paper presents experimental scenarios that use signal Detection Theory (SDT), a well-known tool in psychology research, to capture the perceptions of end users about smart grid technology. If the perceptions of end users are positive, the performance of the smart grid is improved. End user criteria can be analyzed using SDT. In addition, fuzzy logic type 2 is suggested as a way to increase the descriptive power of fuzzy signal Detection Theory. To obtain end user perceptions, several experimental scenarios were created using a didactic smart grid system designed by Delorenzo Group Italy. End users were faced with real situations that enabled determination of their perceptions about the smart grid technology. Experimental results of end users׳ perceptions of smart grid technology are shown using SDT, fuzzy Detection Theory, and fuzzy Detection Theory type 2. The results show that end users have a conservative criterion because they are not entirely confident in the intelligent technology provided by the smart grid; this conservative criterion limits smart grid performance.
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technology transfer motivation analysis based on fuzzy type 2 signal Detection Theory
Ai & Society, 2016Co-Authors: Pedro Ponce, Kenneth Polasko, Arturo MolinaAbstract:This paper presents a complete study based on signal Detection Theory (SDT) for deciding the motivation factors that motivate academic researchers to participate in the technology transfer process (university---industry relationship). Moreover, this study determines the researchers' perception about the motivations strategies designed in universities. The paper focuses on positive motivation factors such as academic prestige, competition, generation of resources, the solution of complex problems, professional challenge, personal gains, personal gratification and the solution of society problems. The negative motivation factors studied in the paper are as follows: innovation environment, time required, and lack of incentive and fear of contravening university policies. The importance of SDT lies in the fact that it is a Theory that can deal with observer perception and the ways in which choices are made. This paper proposes fuzzy sets type 2 in SDT to expand its potential and understand the decision of the researchers during the technology transfer process under conditions of uncertainty. Although fuzzy type 1 Detection Theory (FDT) allows signals to overlap (non-binary description), a complete representation of uncertainty is not incorporated. Thus, fuzzy type 2 signal Detection Theory (FDT2) is proposed to model the uncertainties and noise condition under technology transfer process. High standards of motivation can maintain and attract competent researchers at universities; thus, this paper deals in a deep fashion with all the main aspects about those motivation factors using FDT2.