Applied Bayesian Approaches for Research in Motor Neuron Disease

Frontiers in Neurology

Andreas Hermann¹  Anna G. M. Temp³  Marcel Naumann⁴  Hannes Glaß⁴ 

[1] Center for Transdisciplinary Neurosciences Rostock, University Medical Centre, Rostock, Germany;Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany;Neurozentrum, Berufsgenossenschaftliches Klinikum Hamburg, Hamburg, Germany;Translational Neurodegeneration Section “Albrecht Kossel,” Department of Neurology, University Medical Centre, Rostock, Germany;

关键词: Bayesian statistics;    amyotrophic lateral sclerosis;    single case studies;    clinical trials;    tofersen;    JASP;   

DOI  :  10.3389/fneur.2022.796777

来源: DOAJ

【 摘 要 】

Statistical evaluation of empirical data is the basis of the modern scientific method. Available tools include various hypothesis tests for specific data structures, as well as methods that are used to quantify the uncertainty of an obtained result. Statistics are pivotal, but many misconceptions arise due to their complexity and difficult-to-acquire mathematical background. Even though most studies rely on a frequentist interpretation of statistical readouts, the application of Bayesian statistics has increased due to the availability of easy-to-use software suites and an increased outreach favouring this topic in the scientific community. Bayesian statistics take our prior knowledge together with the obtained data to express a degree of belief how likely a certain event is. Bayes factor hypothesis testing (BFHT) provides a straightforward method to evaluate multiple hypotheses at the same time and provides evidence that favors the null hypothesis or alternative hypothesis. In the present perspective, we show the merits of BFHT for three different use cases, including a clinical trial, basic research as well as a single case study. Here we show that Bayesian statistics is a viable addition of a scientist's statistical toolset, which can help to interpret data.

【 授权许可】

Unknown