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Statistical controversies in clinical research: should schedules of tumor size assessments be changed?
No full textInternational audienceBACKGROUND:Time to progression (TTP) is often used as a primary end point in phase II clinical trials. Since the actual date of nadir and progression is never known, most calculated TTP are overestimated. This study evaluates the imprecision on the estimate of TTP under two hypothetical tumor kinetic settings and various assessment schedules.DESIGN:A two-component tumor growth model was used to account for treatment effect assuming exponential decay for tumor shrinkage and linear growth for progression. Evolution of tumor burden (TB) was modelized according to two scenarios using either a cytotoxic or a cytostatic agent and several assessment schedules. TB, nadir, progression and TTP were simulated for each visit schedule.RESULTS:For cytotoxic agents, our model predicted response at 1.5 weeks, a TB at nadir of 40.2 mm (starting from 100 mm) occurring at 6.7 weeks and true progression at 11.2 weeks with a TB of 48.2 mm. For cytostatic agents, our model predicted no response, a TB at nadir of 77 mm occurring at 9.2 weeks and true progression at 19.4 weeks with a TB of 92 mm. Depending on the assessment schedule, estimated TTP was increased from 0.8 to 36.8 weeks and from 0.6 to 28.6 weeks when compared with the true TTP and varied from 5.2% to 298% and from 1.66 to 109.58% when compared with the true TB at progression for cytotoxic and cytostatic agents, respectively. Our model further shows that for cytotoxic agents, evaluation of TB every 6 weeks is optimal to capture the true nadir, the time to nadir, the true progression and the true TTP, whereas for cytostatic agents, this evaluation is optimal every 10 weeks.CONCLUSIONS:Our results emphasize the importance to estimate the effects of tested drugs on tumor shrinkage before design any phase II clinical trials to choose optimal TB evaluation's timing
« Vie pratique », histoire de la sagesse et polémique philosophique chez Dicéarque
No full textInternational audienceThis paper studies the interplay between the history of mankind, philosophicalpolemics and ethical debates about the best life in the fourth century BCthrough an inquiry into the positions of Dicaearchus of Messana, a pupil of Aristotle,and his disagreement about the best life with Theophrastus. Against recentinterpretations, the paper establishes the various stages in Dicaearchus’ historyof wisdom, its downward path and its criteria to define “philosophy”. This leadsto a better understanding of Dicaearchus’ assessments of the Golden Age, theSeven Sages and Socrates and, above all, of his notion of the “practical life” asnot restricted to politics and as opposed to contemporary scolastic conceptions ofphilosophy probably put forward by Plato and best exemplified by Theophrastusin Dicaearchus’ eyes
Guest editors introduction: special issue on inductive logic programming
No full textInternational audienceThis special issue focuses on the field of Inductive Logic Programming (ILP), which is a subfield of machine learning that uses logic as a uniform representation language for examples, background knowledge and hypotheses. From these roots, ILP's scope has grown to encompass different approaches that address learning from structured relational data. One notable example is the area of statistical relational learning which focuses on extending ILP to model uncertainty. The special issue is also in conjunction with the 24th International Conference on Inductive Logic Programming (ILP), which was held from September 14th to 16th, 2014, in Nancy, France in co-location with ECML/PKDD-2014. To avoid the redundancy between the conference proceedings and the special issue, authors with an accepted paper at ILP were asked to either have their paper appear in the conference proceedings or submit an extended version of the paper to the special issue. While associated with the ILP conference, there was an open call for submissions to this special issue. The special issue received six submissions of which three were originally submitted to the ILP conference. Ultimately, four were accepted to appear in the special issue. The papers offer a nice reflection on the strengths of ILP and relational learning and where the field is headed. Namely, the articles build off ILP's established track record of being particularly well suited to addressing important applications and the vibrant recent work that focuses on modeling uncertainty in relational data. One of the first application areas where ILP gained significant traction was in analyzing molecules, in particular for the task of drug design. Drugs are small molecules that affect disease by binding to a target protein in the human body. Approaches to drug design depend on whether properties and/or structure of the drug or target are known. Previously, ILP has been successfully applied to identify properties of a drug molecule responsible for it binding t
Online Adaptation of the Number of Particles of Sequential Monte Carlo Methods
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''Expression et certification des acquis de l'expérience'' bilan et perspectives Coord : Houot I
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Decentralised consensus-based formation tracking of multiple differential drive robots
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An Evolutionary Approach to solve the Dynamic Multi-Hop Ridematching Problem
No full textInternational audienceThe multihop ridesharing system generates a ridematching solution with an arbitrary number of transfers that respects personal preferences of the users and their time constraints with detour willingness. As it is considered to be NP-complete, an efficient metaheuristic is required in the application to solve the dynamic multihop ridematching problem. In this context, a novel approach, called Metaheuristics Approach Based on Controlled Genetic Operators (MACGeO), which is supported by an original dynamic coding, is developed to address the multihop ridematching problem. The performance of the proposed approach is measured via simulation scenarios, which feature various numbers of carpool drivers (vehicles) and riders (passengers). Experimental results show that the multihop ridematching could greatly increase the number of matched requests while minimizing the number of vehicles required. © The Author(s) 2016
