From Trial and Error to Trial Simulation III: A Framework for Interim Analysis in Efficacy Trials With Antidepressant Drugs

2011 ◽  
Vol 89 (4) ◽  
pp. 602-607 ◽  
Author(s):  
G Santen ◽  
E van Zwet ◽  
P Bettica ◽  
R A Gomeni ◽  
M Danhof ◽  
...  
Keyword(s):  
Interim Analysis ◽  
Antidepressant Drugs ◽  
Trial And Error ◽  
Efficacy Trials ◽  
Trial Simulation

Clinical Trial Simulation: Efficacy Trials

2007 ◽  
pp. 881-900 ◽  
Author(s):  
Matthew M. Riggs ◽  
Christopher J. Godfrey ◽  
Marc R. Gastanguay
Keyword(s):  
Clinical Trial ◽  
Clinical Trial Simulation ◽  
Efficacy Trials ◽  
Trial Simulation

scholarly journals Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter and Stress Hormone Systems

2019 ◽  
Author(s):  
Mariam Bonyadi Camacho ◽  
Warut D. Vijitbenjaronk ◽  
Thomas J Anastasio
Keyword(s):  
Clinical Judgment ◽  
Computational Analysis ◽  
Antidepressant Drugs ◽  
Chronic Administration ◽  
Trial And Error ◽  
Stress Hormone ◽  
Treatment Resistant ◽  
Potential Explanation ◽  
System Components ◽  
Monoaminergic Neurotransmitter

AbstractThe clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavily on clinical judgment and trial-and-error. Unfortunately, the drug combinations prescribed today fail to provide relief for all treatment-resistant depressed patients. In order to identify potentially more effective treatments, we developed a computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmitter-system components (TSCs). We used the model to screen 60 chronically administered drug/hormone pairs and triples, and identified as potentially therapeutic those combinations that raised the monoamines (serotonin, norepinephrine, and dopamine) but lowered cortisol following neuroadaptation in the model. We also evaluated the contributions of individual and pairs of TSCs to therapeutic neuroadaptation with chronic SSRI using sensitivity, correlation, and linear temporal-logic analyses. All three approaches found that therapeutic neuroadaptation to chronic SSRI is an overdetermined process that depends on multiple TSCs, providing a potential explanation for the clinical finding that no single antidepressant regimen alleviates depressive symptoms in all patients.

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

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