Power Analysis for Human Melatonin Suppression Experiments.

experimental design melatonin suppression non-visual effects of ligh power analysis statistical analysis

Journal

Clocks & sleep
ISSN: 2624-5175
Titre abrégé: Clocks Sleep
Pays: Switzerland
ID NLM: 101736579

Informations de publication

Date de publication:
26 Feb 2024
Historique:
received: 29 11 2023
revised: 07 02 2024
accepted: 10 02 2024
medline: 27 3 2024
pubmed: 27 3 2024
entrez: 27 3 2024
Statut: epublish

Résumé

In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a biomarker for this neuroendocrine pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is costly and resource-intensive and may not be feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability in melatonin suppression relevant for practical applications.

Identifiants

pubmed: 38534797
pii: clockssleep6010009
doi: 10.3390/clockssleep6010009
doi:

Types de publication

Journal Article

Langues

eng

Pagination

114-128

Subventions

Organisme : Wellcome Trust
ID : 204686/Z/16/C
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 204686/Z/16/Z
Pays : United Kingdom

Auteurs

Manuel Spitschan (M)

Department of Health and Sport Sciences, TUM School of Medicine and Health, Technical University of Munich, 80992 Munich, Germany.
TUM Institute for Advanced Study (TUM-IAS), Technical University of Munich, 85748 Garching, Germany.
Max Planck Research Group Translational Sensory and Circadian Neuroscience, Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.

Parisa Vidafar (P)

Faculty of Medicine and Health, Central Clinical School, University of Sydney, Sydney, NSW 2006, Australia.
Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia.

Sean W Cain (SW)

Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia.

Andrew J K Phillips (AJK)

Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia.

Ben C Lambert (BC)

Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.

Classifications MeSH