78 research outputs found
A combined local search and integer programming approach to the traveling tournament problem
No full textThe traveling tournament problem is a well-known combinatorial optimization problem with direct applications to sport leagues scheduling, that sparked intensive algorithmic research over the last decade. With the Challenge Traveling Tournament Instances as an established benchmark, the most successful approaches to the problem use meta-heuristics like tabu search or simulated annealing, partially heavily parallelized. Integer programming based methods on the other hand are hardly able to tackle larger benchmark instances. In this work we present a hybrid approach that draws on the power of commercial integer programming solvers as well as the speed of local search heuristics. Our proposed method feeds the solution of one algorithm phase to the other one, until no further improvements can be made. The applicability of this method is demonstrated experimentally on the galaxy instance set, resulting in currently best known solutions for most of the considered instances
sj-docx-1-cpx-10.1177_21677026221101379 – Supplemental material for Change of Threat Expectancy as Mechanism of Exposure-Based Psychotherapy for Anxiety Disorders: Evidence From 8,484 Exposure Exercises of 605 Patients
No full textSupplemental material, sj-docx-1-cpx-10.1177_21677026221101379 for Change of Threat Expectancy as Mechanism of Exposure-Based Psychotherapy for Anxiety Disorders: Evidence From 8,484 Exposure Exercises of 605 Patients by Andre Pittig, Ingmar Heinig, Stephan Goerigk, Jan Richter, Maike Hollandt, Ulrike Lueken, Paul Pauli, Jürgen Deckert, Tilo Kircher, Benjamin Straube, Peter Neudeck, Katja Koelkebeck, Udo Dannlowski, Volker Arolt, Thomas Fydrich, Lydia Fehm, Andreas Ströhle, Christina Totzeck, Jürgen Margraf, Silvia Schneider, Jürgen Hoyer, Winfried Rief, Michelle G. Craske, Alfons O. Hamm and Hans-Ulrich Wittchen in Clinical Psychological Science</p
Intermittent theta burst stimulation in adolescents and young adults with depressive disorders: protocol of a randomized, sham-controlled study with a sequential Bayesian design for adaptive trials
No full textAbstract Intermittent theta burst stimulation (iTBS), a variant of repetitive transcranial magnetic stimulation (rTMS), is an established treatment for adults with major depressive disorder (MDD). Due to its favorable safety profile, iTBS is also a promising early intervention in the transition phase from adolescence to early adulthood, but this has not been systematically investigated to date. Thus, the EARLY-BURST trial investigates the efficacy and safety of iTBS over the left dorsolateral prefrontal cortex (lDLPFC) in treatment-seeking young patients (age 16–26 years) with depressive disorders (i.e. major depressive disorder, persistent depressive disorder, bipolar depression), allowing for relevant co-morbidities. Participants have not received antidepressant or antipsychotic medication during the last 12 months except for short-term (< 2 weeks) on-demand medication. The trial will employ a novel sequential Bayesian, randomized, double-blind, parallel-group, sham-controlled design. Up to 90 patients at two clinical sites (Munich, Augsburg) will be randomized 1:1 to the treatment groups, with sequential analyses starting after 26 patients in each group completed the treatment. The primary outcome will be the difference in depression severity at week 6 (post-treatment visit) between active iTBS and sham iTBS, assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). The trial is planned to be expanded towards a three-arm leapfrog design, contingent on securing additional funding. Thus, in addition to potentially providing evidence of iTBS’s efficacy in adolescents and young adults with depressive disorders, the EARLY-BURST trial aims at setting the stage for subsequent platform trials in this dynamic research field, where novel adaptive study designs are required to meet the need for rapidly testing promising new vs established rTMS protocols. Trial registration: DRKS00033313.Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347Klinikum der Universität Münche
Personalizing transcranial direct current stimulation for treating major depressive disorder
No full textTranscranial direct current stimulation (tDCS) is a safe and efficient intervention for treating major depressive disorder (MDD). However, research has suggested heterogeneity of response between patients. The emerging field of precision psychiatry aims to use statistical modeling of multi-modal data to tailor treatment to the single patient.
To this end, more in-depth analysis of randomized controlled trials (RCTs) will be relevant (1) due to limited availability of other large datasets with high phenotypic detail and (2) to develop tools for personalization within counterfactually controlled environments (i.e. experimental designs with sham intervention and/or active treatment comparison) to distinguish specific vs. non-specific patterns in treatment data.
Previous research has aimed at identifying patient-related factors associated with better response. However, most analyses have operated on the group-level, ignoring natural clusters within the patients' constituting factors, their individual trajectories of symptom improvement, and their presented symptoms. Furthermore, group-based modeling strategies were limited to explanatory approaches using in-sample hypothesis-testing, that are ill-suited to prognosticate outcomes of single patients.
This dissertation provides a methodological framework for reevaluation of existing clinical trial data (1) to provide future investigations with more differentiated units of analysis and (2) to complement explanatory approaches with predictive modeling strategies enabling prediction of single-patient outcomes. Using data from a landmark 3-arm clinical trial paradigmatic for a rigorously controlled experimental design (10-week treatment of tDCS vs. escitalopram vs. placebo) the dissertation provides three blueprint studies for modeling heterogeneity of tDCS response:
Study 1 characterized response to tDCS by considering patient-individual dynamics of symptom change over the course of treatment. Distinct trajectories of tDCS response could be identified (rapid-, slow-, and no/minimal improvement), representing patient subgroups with varying strength and speed of improvement. These results suggest development of individualized treatment protocols and exploration of prolonged treatment courses.
Study 2 reevaluated the efficacy of tDCS, in distinct, naturally occurring clusters of depressive symptoms. Using unsupervised machine learning (ML), a global depression measure (HAM-D) was parsed into 4 distinct symptom clusters. Analysis of cluster-scores showed superiority of tDCS and escitalopram over placebo in core depressive symptoms, but only tDCS was superior in improving sleep and only escitalopram was superior in improving guilt/anxiety symptoms, suggesting treatment selection based on patients' symptom profiles.
In Study 3 supervised ML algorithms were employed to predict response to tDCS. In this proof-of-concept approach, response could be predicted above chance on the single-patient level, but overall accuracy was modest. Features employed for model training were explored using interpretable ML methods. Trained algorithms were provided to the field for expansion as well as tests of generalizability and incremental utility.
The presented studies illustrate how in-depth secondary analyses of clinical trial data can aid personalization of treatment. The provided methodological framework can be expanded (options are discussed) and generalized to other contexts and interventions that show heterogeneity of treatment effects.
Yet, the empirical studies also epitomize challenges precision psychiatry is faced with, including low data availability, low outcome granularity, and limited external validation opportunities.
The dissertation concludes with a discussion of challenges and future directions resulting from infrastructural demands in data acquisition, data management, data sharing, and interdisciplinary collaboration
A combined local search and integer programming approach to the traveling tournament problem
No full textThe traveling tournament problem is a well-known combinatorial optimization problem with direct applications to sport leagues scheduling, that sparked intensive algorithmic research over the last decade. With the Challenge Traveling Tournament Instances as an established benchmark, the most successful approaches to the problem use meta-heuristics like tabu search or simulated annealing, partially heavily parallelized. Integer programming based methods on the other hand are hardly able to tackle larger benchmark instances. In this work we present a hybrid approach that draws on the power of commercial integer programming solvers as well as the speed of local search heuristics. Our proposed method feeds the solution of one algorithm phase to the other one, until no further improvements can be made. The applicability of this method is demonstrated experimentally on the galaxy instance set, resulting in currently best known solutions for most of the considered instances
A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions
No full textWe present the GMTKN55 benchmark database for general main group thermochemistry, kinetics and noncovalent interactions. Compared to its popular predecessor GMTKN30 [Goerigk and Grimme J. Chem. Theory Comput., 2011, 7, 291], it allows assessment across a larger variety of chemical problems—with 13 new benchmark sets being presented for the first time—and it also provides reference values of significantly higher quality for most sets. GMTKN55 comprises 1505 relative energies based on 2462 single-point calculations and it is accessible to the user community via a dedicated website. Herein, we demonstrate the importance of better reference values, and we re-emphasise the need for London-dispersion corrections in density functional theory (DFT) treatments of thermochemical problems, including Minnesota methods. We assessed 217 variations of dispersion-corrected and -uncorrected density functional approximations, and carried out a detailed analysis of 83 of them to identify robust and reliable approaches. Double-hybrid functionals are the most reliable approaches for thermochemistry and noncovalent interactions, and they should be used whenever technically feasible. These are, in particular, DSD-BLYP-D3(BJ), DSD-PBEP86-D3(BJ), and B2GPPLYP-D3(BJ). The best hybrids are ωB97X-V, M052X-D3(0), and ωB97X-D3, but we also recommend PW6B95-D3(BJ) as the best conventional global hybrid. At the meta-generalised-gradient (meta-GGA) level, the SCAN-D3(BJ) method can be recommended. Other meta-GGAs are outperformed by the GGA functionals revPBE-D3(BJ), B97-D3(BJ), and OLYP-D3(BJ). We note that many popular methods, such as B3LYP, are not part of our recommendations. In fact, with our results we hope to inspire a change in the user community's perception of common DFT methods. We also encourage method developers to use GMTKN55 for cross-validation studies of new methodologies
A faster exact method for solving the robust multi-mode resource-constrained project scheduling problem
No full textThis paper presents a mixed-integer linear programming formulation for the multi-mode resource-constrained project scheduling problem with uncertain activity durations. We consider a two-stage robust optimisation approach and find solutions that minimise the worst-case project makespan, whilst assuming that activity durations lie in a budgeted uncertainty set. Computational experiments show that this easy-to-implement formulation is many times faster than the current state-of-the-art solution approach for this problem, whilst solving over 40% more instances to optimality over the same benchmarking set. © 2022 The Author(s
Ranking robustness and its application to evacuation planning
No full textAbstractWe present a new approach to handle uncertain combinatorial optimization problems that uses solution ranking procedures to determine the degree of robustness of a solution. Unlike classic concepts for robust optimization, our approach is not purely based on absolute quantitative performance, but also includes qualitative aspects that are of major importance for the decision maker.We discuss the two variants, solution ranking and objective ranking robustness, in more detail, presenting problem complexities and solution approaches. Using an uncertain shortest path problem as a computational example, the potential of our approach is demonstrated in the context of evacuation planning due to river flooding
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