Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Maximilian Linde"'
Autor:
Maximilian Linde, Don van Ravenzwaaij
Publikováno v:
BMC Medical Research Methodology, Vol 23, Iss 1, Pp 1-14 (2023)
Abstract Background Clinical trials often seek to determine the superiority, equivalence, or non-inferiority of an experimental condition (e.g., a new drug) compared to a control condition (e.g., a placebo or an already existing drug). The use of fre
Externí odkaz:
https://doaj.org/article/164adbb6da484c34b167a133c284a773
Autor:
Joyce M Hoek, Sarahanne M Field, Ymkje Anna de Vries, Maximilian Linde, Merle-Marie Pittelkow, Jasmine Muradchanian, Don van Ravenzwaaij
Publikováno v:
PLoS ONE, Vol 16, Iss 7, p e0255093 (2021)
BackgroundFollowing testing in clinical trials, the use of remdesivir for treatment of COVID-19 has been authorized for use in parts of the world, including the USA and Europe. Early authorizations were largely based on results from two clinical tria
Externí odkaz:
https://doaj.org/article/36be28fe94e5467cae4e8961dba6bdd3
Autor:
Johnny van Doorn, Julia M. Haaf, Angelika Marlene Stefan, Eric-Jan Wagenmakers, Gregory Edward Cox, Clintin P. Davis-Stober, Andrew Heathcote, Daniel W. Heck, Michael Kalish, David Kellen, Dora Matzke, Richard Donald Morey, Bruno Nicenboim, Don van Ravenzwaaij, Jeffrey N. Rouder, Daniel Schad, Rich Shiffrin, Henrik Singmann, Shravan Vasishth, João Veríssimo, Florence Bockting, Suyog Chandramouli, John C Dunn, Quentin Frederik Gronau, Maximilian Linde, Sara D McMullin, Danielle Navarro, Martin Schnuerch, Himanshu Yadav, Frederik Aust
Publikováno v:
Computational Brain and Behavior, 6(1). Springer
van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::913478eb8d898d23dae77ff0f528e81d
https://doi.org/10.1007/s42113-022-00160-3
https://doi.org/10.1007/s42113-022-00160-3
Autor:
Don van Ravenzwaaij, Maximilian Linde
Publikováno v:
Computational Brain & Behavior, 6, 14-27. Springer
Nested data structures, in which conditions include multiple trials and are fully crossed with participants, are often analyzed using repeated-measures analysis of variance or mixed-effects models. Typically, researchers are interested in determining
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81800c1c86e46705a7053a9dcfe58724
https://research.rug.nl/en/publications/6dbbcdce-b1bb-4cc0-8695-c4758e0f8ca5
https://research.rug.nl/en/publications/6dbbcdce-b1bb-4cc0-8695-c4758e0f8ca5
The push for open science has produced many initiatives that arguably improve the quality of science, such as preregistration of research methods, more general availability of scientific articles in the form of preprints and open access articles, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df3d138722653155c078083f7b5446a1
https://osf.io/preprints/metaarxiv/agvnx
https://osf.io/preprints/metaarxiv/agvnx
The use of Cox proportional hazards regression to analyze time-to-event data is ubiquitous in biomedical research. Typically, the frequentist framework is used to draw conclusions about whether hazards are different between patients in an experimenta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb2afc2b3acc1575fbe1eb916f703bf7
Decision makers often combine multiple pieces of information to make performance predictions and hiring decisions. More valid predictions are made when information is combined algorithmically (mechanical prediction) rather than in the decision-maker
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85e55733dba8817245ca919602e00de6
https://doi.org/10.31234/osf.io/743hn
https://doi.org/10.31234/osf.io/743hn
Autor:
Daniel W. Heck, Udo Boehm, Florian Böing-Messing, Paul - Christian Bürkner, Koen Derks, Zoltan Dienes, Qianrao Fu, Xin Gu, Diana Karimova, Henk Kiers, Irene Klugkist, Rebecca M. Kuiper, Michael David Lee, Roger Leenders, Hidde Jelmer Leplaa, Maximilian Linde, Alexander Ly, Marlyne Meijerink-Bosman, Mirjam Moerbeek, Joris Mulder, Bence Palfi, Felix D. Schönbrodt, Jorge Tendeiro, Don van den Bergh, Caspar J. Van Lissa, Don van Ravenzwaaij, wolf vanpaemel, Eric-Jan Wagenmakers, Donald Ray Williams, Marielle Zondervan-Zwijnenburg, Herbert Hoijtink
Publikováno v:
Tilburg University-PURE
Psychological Methods
Psychological Methods. AMER PSYCHOLOGICAL ASSOC
Psychological Methods
Psychological Methods. AMER PSYCHOLOGICAL ASSOC
The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8964010a83b30d225040cf0c439d85ce
https://doi.org/10.1037/met0000454
https://doi.org/10.1037/met0000454
Publikováno v:
Psychological Methods. AMER PSYCHOLOGICAL ASSOC
Linde et al. (2021) compared the "two one-sided tests", the "highest density interval -- region of practical equivalence", and the "interval Bayes factor" approaches to establishing equivalence in terms of power and Type I error rate using typical de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef1589d9eb662eb9140a12f7d5de1525
https://research.rug.nl/en/publications/2cf0f398-f9d5-4e9b-b851-18bb1c5c145d
https://research.rug.nl/en/publications/2cf0f398-f9d5-4e9b-b851-18bb1c5c145d
Publikováno v:
37th Annual Conference of the Society for Industrial and Organizational Psychology
University of Groningen
University of Groningen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::185b6d31cfcfca54eb216adc305a09ff
https://research.rug.nl/en/publications/4a5f888b-d88d-4492-9377-c66be14fde29
https://research.rug.nl/en/publications/4a5f888b-d88d-4492-9377-c66be14fde29