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Institution

University of Amsterdam

EducationAmsterdam, Noord-Holland, Netherlands
About: University of Amsterdam is a education organization based out in Amsterdam, Noord-Holland, Netherlands. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 59309 authors who have published 140894 publications receiving 5984137 citations. The organization is also known as: UvA & Universiteit van Amsterdam.


Papers
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Journal ArticleDOI
TL;DR: A nested case–control study design that involves prospective collection of specimens before outcome ascertainment from a study cohort that is relevant to the clinical application and common biases that pervade the biomarker research literature would be eliminated if rigorous standards were followed.
Abstract: With the emergence of new molecular biotechnologies, the intensity of biomarker research has increased greatly, yet the scientific rigor of biomarker research lags far behind that of therapeutic research ( 1 – 3 ). In therapeutic research, the randomized placebocontrolled, blinded clinical trial takes the central role in pivotal evaluation of a new therapy. Standards for the conduct of such studies have been agreed upon internationally ( 4 ) ( www.ich.org ), and books and journals have been devoted to nuances of their design and analysis. Analogously, in this commentary, we consider key aspects of design for pivotal evaluation of a biomarker ’ s capacity to correctly classify a subject’s outcome (ie, classification accuracy), in which a subject’s outcome may be his or her current disease status or a future event for him or her. We propose a prospective-specimencollection, retrospective-blinded-evaluation (PRoBE) design in which biologic specimens are collected prospectively from a cohort that represents the target population that is envisioned for clinical application of the biomarker. Specimens and clinical data are collected in the absence of knowledge about patient outcome. After outcome status is ascertained, case patients with the outcome and control subjects without it are selected randomly from the cohort and their specimens are assayed for the biomarker in a fashion that is blinded to case – control status. Although every biomarker study has its own special considerations, as does every randomized clinical trial, we propose that elucidation of the key design issues in this commentary will help move the field toward standards of practice. Biomarkers are developed for many different purposes, including for classifi cation and prediction, as surrogate outcomes in clinical trials, as measures of toxic or preventive exposures, or as a guide to individual treatment choice ( 5 ). In this commentary, we consider only the fi rst category, which includes diagnostic, screening, and prognostic markers. A diagnostic marker is used in people with signs or symptoms to aid in assessing whether they have a condition. A screening marker is used in asymptomatic people to detect a disease or condition at an early stage. A prognostic marker is used in subjects diagnosed with a condition to predict subsequent outcomes, such as disease recurrence or progression. The PRoBE design is intended for all of these applications. Development of a biomarker is a process that begins with biomarker discovery, is followed by rigorous evaluation of classifi cation accuracy, and then terminates with the evaluation of the impact of the biomarker on clinical outcomes ( 6 , 7 ). Multiple studies may be involved in each stage. For example, in the discovery stage, a sequence of studies may be performed to identify biomarkers from among a large pool of candidates, to validate these individual markers in independent samples, and to optimally combine markers from a panel. We focus on the intermediate stage, after all discovery work is completed. We assume that a well-defi ned biomarker, or marker combination, is to be defi nitively evaluated for its classifi cation accuracy in a specifi c clinical application. If it can be shown that the marker has acceptable classifi cation accuracy, the marker should move to the fi nal stage of evaluation, in which the net benefi t to patients is assessed by incorporating effects of clinical interventions recommended for patients on the basis of their biomarker results. Clearly, before biomarker results are used in making medical decisions for individuals, it is crucial to know how accurately the marker classifi es or predicts their outcomes.

622 citations

Journal ArticleDOI
TL;DR: The Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) as discussed by the authors is an improvement over the SCEM-UA global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution.
Abstract: [1] Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.

622 citations

Journal ArticleDOI
TL;DR: Evidence was obtained that the high replication rate of syncytium-inducing isolates observed during primary isolation may be due to higher infectivity of these isolates, and the finding that only syncyTium- inducing isolates could be transmitted to the H9 cell line is compatible with thisHigher infectivity.
Abstract: Human immunodeficiency virus isolates were studied with respect to syncytium-inducing capacity, replicative properties, and host range. Five of 10 isolates from patients with acquired immunodeficiency syndrome (AIDS) and AIDS-related complex were able to induce syncytia in cultures of peripheral blood mononuclear cells (MNC). In contrast, only 2 of 12 isolates from asymptomatic individuals had syncytium-inducing capacity. Syncytium-inducing isolates were reproducibly obtained from the same MNC sample in over 90% of the cases, independent of the donor MNC used for propagation. Syncytium-inducing capacity was shown to be a stable property of an isolate, independent of viral replication rates. Evidence was obtained that the high replication rate of syncytium-inducing isolates observed during primary isolation may be due to higher infectivity of these isolates. The finding that only syncytium-inducing isolates could be transmitted to the H9 cell line is compatible with this higher infectivity. The frequent isolation of syncytium-inducing isolates from individuals with AIDS-related complex or AIDS and the apparent higher in vitro infectivity of these isolates suggest that syncytium-inducing isolates may unfavorably influence the course of human immunodeficiency virus infection.

621 citations

Journal ArticleDOI
TL;DR: An updated diagnostic algorithm for EoE was developed, with removal of the PPI trial requirement, and the evidence suggests that PPIs are better classified as a treatment for esophageal eosinophilia that may be due to EOE than as a diagnostic criterion.

621 citations

Journal ArticleDOI
TL;DR: Two distinct genome-wide association studies of ulcerative colitis are presented and their joint analysis with a previously published scan shows that roughly half of the known Crohn's disease associations are shared with ulceratives colitis, thereby providing insight into disease pathogenesis.
Abstract: Ulcerative colitis is a chronic, relapsing inflammatory condition of the gastrointestinal tract with a complex genetic and environmental etiology. In an effort to identify genetic variation underlying ulcerative colitis risk, we present two distinct genome-wide association studies of ulcerative colitis and their joint analysis with a previously published scan, comprising, in aggregate, 2,693 individuals with ulcerative colitis and 6,791 control subjects. Fifty-nine SNPs from 14 independent loci attained an association significance of P < 10(-5). Seven of these loci exceeded genome-wide significance (P < 5 x 10(-8)). After testing an independent cohort of 2,009 cases of ulcerative colitis and 1,580 controls, we identified 13 loci that were significantly associated with ulcerative colitis (P < 5 x 10(-8)), including the immunoglobulin receptor gene FCGR2A, 5p15, 2p16 and ORMDL3 (orosomucoid1-like 3). We confirmed association with 14 previously identified ulcerative colitis susceptibility loci, and an analysis of acknowledged Crohn's disease loci showed that roughly half of the known Crohn's disease associations are shared with ulcerative colitis. These data implicate approximately 30 loci in ulcerative colitis, thereby providing insight into disease pathogenesis.

621 citations


Authors

Showing all 59759 results

NameH-indexPapersCitations
Richard A. Flavell2311328205119
Scott M. Grundy187841231821
Stuart H. Orkin186715112182
Kenneth C. Anderson1781138126072
David A. Weitz1781038114182
Dorret I. Boomsma1761507136353
Brenda W.J.H. Penninx1701139119082
Michael Kramer1671713127224
Nicholas J. White1611352104539
Lex M. Bouter158767103034
Wolfgang Wagner1562342123391
Jerome I. Rotter1561071116296
David Cella1561258106402
David Eisenberg156697112460
Naveed Sattar1551326116368
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023198
2022698
20219,648
20208,534
20197,822
20186,407