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Walter Van Dyck - One of the best experts on this subject based on the ideXlab platform.

  • real world Evidence Gathering in oncology the need for a biomedical big data insight providing federated network
    Frontiers in Medicine, 2019
    Co-Authors: Tine Geldof, Isabelle Huys, Walter Van Dyck
    Abstract:

    Moving towards new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines’ effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world Evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V’s of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V’s whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.

Tine Geldof - One of the best experts on this subject based on the ideXlab platform.

  • real world Evidence Gathering in oncology the need for a biomedical big data insight providing federated network
    Frontiers in Medicine, 2019
    Co-Authors: Tine Geldof, Isabelle Huys, Walter Van Dyck
    Abstract:

    Moving towards new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines’ effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world Evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V’s of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V’s whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.

Isabelle Huys - One of the best experts on this subject based on the ideXlab platform.

  • real world Evidence Gathering in oncology the need for a biomedical big data insight providing federated network
    Frontiers in Medicine, 2019
    Co-Authors: Tine Geldof, Isabelle Huys, Walter Van Dyck
    Abstract:

    Moving towards new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines’ effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world Evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V’s of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V’s whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.

Scott A Moss - One of the best experts on this subject based on the ideXlab platform.

  • litigation discovery cannot be optimal but could be better the economics of improving discovery timing in a digital age
    Duke Law Journal, 2009
    Co-Authors: Scott A Moss
    Abstract:

    Cases are won and lost in discovery, yet discovery draws surprisingly little academic attention. Most scholarship focuses on how much discovery to allow, not how courts decide discovery disputes – which, unlike trials, occur in most cases. Today, much Evidence is “e-discovery” – imprudent emails or stilllingering “deleted” files – making costly discovery battles increasingly salient. But the e-discovery rules are not truly new, just a strengthening of old cost/benefit “proportionality” limits on discovery enacted when the spread of photocopiers similarly increased the amount of discovery. Proportionality limits are topic of broad consensus among civil procedure scholars as well as economists concerned that discovery excess yields extortionate settlements and results from parties ignoring costs they impose on others. Contrary to the consensus, this Article deems proportionality rules impossible to apply effectively. By both failing to curb discovery excess and disallowing discovery that meritorious cases need, ineffective proportionality limits let bad cases predominate over good cases, just as the bad can drive out the good in product markets. This Article acknowledges proportionality’s flaws but rejects the consensus blaming bad rulemaking or judging. Rather, proportionality requires impossible comparisons: how can courts compare discovery cost to evidentiary value before the parties gather the Evidence? Like other arguments that procedural rulings are never truly separate from case merits, this Article explains how discovery has more probative value in close cases than in the strongest and weakest cases. Yet case merits remain uncertain in discovery, when courts are not yet able to examine all the Evidence. In game theory terms, parties with discovery disputes cannot convey case merit credibly; courts have too little information, so low-merit parties can claim high merit, and courts act as if all cases warrant similar discovery. In this “pooling equilibrium,” ruling the same on all cases in the “pool,” regardless of merit, is courts’ best strategy but a sub-optimal one, yielding too much discovery in low-merit cases, too little in higher-merit cases. Thus, the quest for better discovery has disappointed not because of bad rules or cases, but because courts and parties are stuck in a pooling equilibrium. This is an information-timing circularity: optimal Evidence-Gathering requires merits analysis, which requires more Evidence-Gathering. One answer is to defer close decisions on possibly useful but costly Evidence until meritorious cases separate from the pool, turning a pooling equilibrium into a “separating equilibrium.” Summary judgment can be this separating point: cases going to trial after summary judgment likely have 50/50 odds – better than most cases. Costly Evidence has more value in a 50/50 case, where the jury will struggle to reach a verdict, than a very weak (or very strong) case. Nobody yet has proposed solving the costly discovery dilemma with post-summary judgment discovery (summary judgment typically follows discovery), but high-cost Evidence can be an exception: cases surviving summary judgment are the close calls warranting more fact-Gathering, so some costly discovery commonly denied now should be allowed later, after summary judgment. While imperfect, this solution could improve the status quo, and imperfection is inevitable given the fundamental information timing problem that prevents accurate proportionality decisions. Thus, the prevailing debate too narrowly focuses on discovery quantity and could benefit from focusing more on discovery timing. Interestingly, existing rules give courts the discretion to use this proposal, but a new rule could minimize the risk of courts misusing the proposal to deny more discovery. This Article concludes by noting how economic analysis of litigation must do more than prescribe costbenefit comparisons; it must also consider the details and information timing of the litigation process. LITIGATION DISCOVERY CANNOT BE OPTIMAL BUT COULD BE BETTER: THE ECONOMICS OF IMPROVING DISCOVERY TIMING IN A DIGITAL AGE

  • litigation discovery cannot be optimal but could be better the economics of improving discovery timing in a digital age
    Social Science Research Network, 2008
    Co-Authors: Scott A Moss
    Abstract:

    Cases are won and lost in discovery, yet discovery draws too little academic attention. Most scholarship focuses on how much discovery to allow, not how courts decide discovery disputes - which, unlike trials, occur in most cases. The growth of e-discovery - imprudent emails or lingering deleted files - makes cost issues increasingly salient, but the e-discovery rules just reiterate existing cost/benefit proportionality limits. Proportionality limits are topic of broad consensus among civil procedure scholars and economists, but this Article deems them impossible to apply effectively. Proportionality limits fail to curb discovery excess while also disallowing discovery meritorious cases need, resulting in bad cases dominating good ones. This Article acknowledges proportionality's flaws but rejects the consensus blaming bad rulemaking or judging. Rather, proportionality requires impossible comparisons: how can courts compare discovery value and cost before parties gather the Evidence? Like other arguments that procedural rulings are never truly separate from case merits, this Article notes how discovery has more probative value in the closest cases - yet case merits remain uncertain in discovery, when courts cannot yet examine all the Evidence. In game theory terms, parties with discovery disputes cannot convey case merit credibly; courts have too little information, so low-merit parties can claim high merit, and courts act as if all cases warrant similar discovery. In this pooling equilibrium, ruling the same on all cases in the pool, regardless of merit, is courts' best strategy but a sub-optimal one, yielding too much discovery in low-merit cases, too little in higher-merit ones. Thus, the quest for better discovery has disappointed not because of bad rules or decisions, but because courts and parties are stuck in a pooling equilibrium with information-timing circularity: optimal Evidence-Gathering requires merits analysis, which requires Evidence-Gathering.One answer is to defer close decisions on possibly useful but costly Evidence until meritorious cases separate from the pool, turning pooling equilibria into separating equilibria. Summary judgment can be this separation: cases going to trial, post-summary judgment, likely have 50/50 odds - better than most. Costly Evidence has more value in 50/50 cases, where juries struggle to reach verdicts, than in weaker or stronger cases. No one yet has proposed post-summary judgment discovery to redress the costly discovery dilemma (summary judgment typically follows discovery), but high-cost Evidence can be an exception: cases surviving summary judgment are close calls warranting more fact-Gathering, so some costly discovery regularly denied now should be allowed after summary judgment. Thus, the existing debate is too focused on discovery quantity; it should focus more on discovery timing. Existing rules give courts discretion to use this proposal, but a new rule could minimize the risk of misusing the proposal to deny more discovery. This Article concludes by briefly noting how economic analyses must consider the details and information timing of the litigation process.

Jose M Molina - One of the best experts on this subject based on the ideXlab platform.

  • a stopping criterion for multi objective optimization evolutionary algorithms
    Information Sciences, 2016
    Co-Authors: Luis Marti, Jesus Garcia, Antonio Berlanga, Jose M Molina
    Abstract:

    This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for Evidence-Gathering purposes. The MDR indicator, which is also introduced, is a special-purpose progress indicator designed for the purpose of stopping a multi-objective optimization. As part of the paper we describe the criterion from a theoretical perspective and examine its performance on a number of test problems. We also compare this method with similar approaches to the issue. The results of these experiments suggest that MGBM is a valid and accurate approach.

  • an approach to stopping criteria for multi objective optimization evolutionary algorithms the mgbm criterion
    Congress on Evolutionary Computation, 2009
    Co-Authors: Luis Marti, Jesus Garcia, Antonio Berlanga, Jose M Molina
    Abstract:

    In this work we put forward a comprehensive study on the design of global stopping criteria for multi-objective optimization. We describe a novel stopping criterion, denominated MGBM criterion that combines the mutual domination rate (MDR) improvement indicator with a simplified Kalman filter that is used for Evidence Gathering process. The MDR indicator, which is introduced along, is a special purpose solution meant for the stopping task. It is capable of gauging the progress of the optimization with a low computational cost and therefore suitable for solving complex or many-objective problems. The viability of the proposal is established by comparing it with some other possible alternatives. It should be noted that, although the criteria discussed here are meant for MOPs and MOEAs, they could be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.