Obtained effect size as a function of sample size in approved antidepressants
Autor: | John A. Whitaker, Michael Gibertini, Kari Rene Nations |
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Rok vydání: | 2012 |
Předmět: |
Research design
medicine.medical_specialty Databases Factual Population MEDLINE Affect (psychology) Sampling Studies Odds Clinical Trials Phase II as Topic medicine Humans Neurotransmitter Uptake Inhibitors Pharmacology (medical) education Psychiatry Drug Approval Randomized Controlled Trials as Topic Depressive Disorder Major education.field_of_study United States Food and Drug Administration Drugs Investigational Antidepressive Agents United States Clinical trial Psychiatry and Mental health Clinical Trials Phase III as Topic Research Design Sample size determination Antidepressant Controlled Clinical Trials as Topic Psychology |
Zdroj: | International Clinical Psychopharmacology. 27:100-106 |
ISSN: | 0268-1315 |
DOI: | 10.1097/yic.0b013e32834f504f |
Popis: | The high failure rate of antidepressant trials has spurred exploration of the factors that affect trial sensitivity. In the current analysis, Food and Drug Administration antidepressant drug registration trial data compiled by Turner et al. is extended to include the most recently approved antidepressants. The expanded dataset is examined to further establish the likely population effect size (ES) for monoaminergic antidepressants and to demonstrate the relationship between observed ES and sample size in trials on compounds with proven efficacy. Results indicate that the overall underlying ES for antidepressants is approximately 0.30, and that the variability in observed ES across trials is related to the sample size of the trial. The current data provide a unique real-world illustration of an often underappreciated statistical truism: that small N trials are more likely to mislead than to inform, and that by aligning sample size to the population ES, risks of both erroneously high and low effects are minimized. The results in the current study make this abstract concept concrete and will help drug developers arrive at informed gate decisions with greater confidence and fewer risks, improving the odds of success for future antidepressant trials. |
Databáze: | OpenAIRE |
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