scholarly journals DEFORMATION BASED MORPHOMETRY STUDY OF LONGITUDINAL MRI CHANGES IN BEHAVIORAL VARIANT FRONTOTEMPORAL DEMENTIA

2019 ◽  
Author(s):  
Ana L. Manera ◽  
Mahsa Dadar ◽  
D. Louis Collins ◽  
Simon Ducharme ◽  
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Disease Progression ◽  
Frontotemporal Dementia ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Cross Sectional ◽  
One Year ◽  
Deformation Based Morphometry

ABSTRACTObjectiveTo objectively quantify how cerebral volume loss could assist with clinical diagnosis and clinical trial design in the behavioural variant of frontotemporal dementia (bvFTD).MethodsWe applied deformation-based morphometric analyses with robust registration to precisely quantify the magnitude and pattern of atrophy in patients with bvFTD as compared to cognitively normal controls (CNCs), to assess the progression of atrophy over one year follow up and to generate clinical trial sample size estimates to detect differences for the structures most sensitive to change. This study included 203 subjects - 70 bvFTD and 133 CNCs - with a total of 482 timepoints from the Frontotemporal Lobar Degeneration Neuroimaging Initiative.ResultsDeformation based morphometry (DBM) revealed significant atrophy in the frontal lobes, insula, medial and anterior temporal regions bilaterally in bvFTD subjects compared to controls with outstanding subcortical involvement. We provide detailed information on regional changes per year. In both cross-sectional analysis and over a one-year follow-up period, ventricle expansion was the most prominent differentiator of bvFTD from controls and a sensitive marker of disease progression.ConclusionsAutomated measurement of ventricular expansion is a sensitive and reliable marker of disease progression in bvFTD to be used in clinical trials for potential disease modifying drugs, as well as possibly to implement in clinical practice. Ventricular expansion measured with DBM provides the lowest published estimated sample size for clinical trial design to detect significant differences over one and two years.

scholarly journals Implications of Clinical Trial Design on Sample Size Requirements

2007 ◽  
Vol 34 (4) ◽  
pp. 664-669 ◽  
Author(s):  
A. C. Leon
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Trial Design ◽  
Clinical Trial Design

Clinical trial design and sample size calculation

2012 ◽  
pp. 356-367
Author(s):  
Rebecca Richards-Kortum
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Trial Design ◽  
Sample Size Calculation ◽  
Clinical Trial Design

Could a biomarkers-based patients' selection help to plan a clinical trial with targeted agents (TA) for metastatic renal cell carcinoma (mRCC)? Interaction and power analysis of randomized trials (RCTs).

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 399-399
Author(s):  
Francesco Massari ◽  
Francesca Maines ◽  
Sara Pilotto ◽  
Maria Bonomi ◽  
Diana Giannarelli ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Overall Survival ◽  
Sample Size ◽  
Power Analysis ◽  
Trial Design ◽  
Random Effect ◽  
Random Effect Model ◽  
Clinical Trial Design ◽  
Attrition Rate ◽  
Intention To Treat

399 Background: To predict the clinical benefit of TA in mRCC, several studies have analyzed some angiogenesis-related biomarkers including soluble VEGF and VEGF-Receptors, VEGF polymorphisms, IL-8 polymorphisms, and VHL mutations. We performed a power analysis to assess how much a biomarker-based approach can affect the sample size of a trial in mRCC. Methods: Hazard ratios (HR) for survival with 95% confidence intervals (CI) for overall survival were extracted and cumulated according to a random-effect model from RCTs. A sensitivity analysis according to ‘biomarker-selection’ approach and to ‘unselected’ fashion was accomplished in order to test for interaction. Testing for heterogeneity was performed as well. Results: A correlation between a featured biomarker and treatment effect was reported in three RCTs (1987 patients). The attrition rate for the survival analysis according to the molecular analysis was 45% (range 20 to 60%). In spite of the extremely different bio-markers’ profiles, demonstrated by the significant observed heterogeneity (p = 0.002), we found an Hazard Ratio (HR) for overall survival of 0.60 (95% CI 0.40-0.89; p = 0.013) and a significant interaction according to strategy (‘biomarker-selected’ vs.‘unselected’) (p = 0.025), supporting a differential effect of these drugs when administered according to a predictive phenotype. In the ‘unselected’ sample, considering the intention to treat analysis (all randomized patients) the HR was 0.90 (95% 0.80-1.00; p = 0.06). From a clinical trial design perspective, if targeting these expected survival differences, with a power 80%, and a alpha-error 0.05, the required events are described in the table. Conclusions: With the aim to avoid the attrition of the heterogeneity of biomarkers’ selection and drugs, we speculate that the sample size of a planned clinical trial design in mRCC according to a biomolecular classifier may help to shorten the gap between clinical research and practice. [Table: see text]

A Two-Stage Adaptive Targeted Clinical Trial Design for Biomarker Performance-Based Sample Size Re-Estimation

2016 ◽  
Vol 8 (1) ◽  
pp. 66-76 ◽  
Author(s):  
Zhong Gao ◽  
Anindya Roy ◽  
Ming Tan
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Two Stage

scholarly journals INTEREST GROUP SESSION—GEROSCIENCE: METHODS FROM BENCH TO POPULATION SCIENCE TO INFORM CONSTRUCTION OF GEROSCIENCE CLINICAL TRIALS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S745-S745
Author(s):  
Jason L Sanders ◽  
Anne B Newman
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Biological Pathway ◽  
Efficacy And Safety ◽  
Population Science ◽  
Multiple Levels ◽  
Aging Mechanisms

Abstract We are on the cusp of a revolution in aging science. It has matured to the point where geroscience trials will test interventions in humans which alter aging mechanisms to lengthen healthspan and possibly lifespan. This goal is unprecedented in clinical trial design, and it requires retooling the clinical trial toolbox. Traditionally, trials are constructed around a single disease; interventions target a narrow part of a defined biological pathway involving only one molecule, tissue, or organ; events are well known intermediate endpoints and clinically-defined hard outcomes; and follow up may be short and historically informed based on prior trials. Geroscience trials by design target aging mechanisms which, when altered, are likely to have pleiotropic effects that modify several biologic pathways; efficacy and safety signals may require integration across multiple levels of biologic organization; intermediate endpoints are not agreed upon; and follow up timelines are undefined. In this symposium, we provide guidance on the design of geroscience trials using examples that span from bench to population science. Dr. LeBrasseur will discuss screening senolytic compounds across models of age-associated decline and advancing their candidacy as interventions. Dr. Justice will detail a framework for biomarker selection in geroscience trials, focusing on a trial of metformin as an example. Dr. Sanders will illustrate how observational data can inform phenotype use in clinical trials. Dr. Levine will explain translating omics data for use in geroscience trials, focusing on epigenomics. We expect additional discussion to hasten development of well-designed geroscience trials.

A prospective one-year natural history study of mucopolysaccharidosis types IIIA and IIIB: Implications for clinical trial design

2016 ◽  
Vol 119 (3) ◽  
pp. 239-248 ◽  
Author(s):  
K.V. Truxal ◽  
H. Fu ◽  
D.M. McCarty ◽  
K.A. McNally ◽  
K.L. Kunkler ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Natural History ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Natural History Study ◽  
One Year

scholarly journals Defining prior therapy in myelodysplastic syndromes and criteria for relapsed and refractory disease: implications for clinical trial design and enrollment

Blood ◽  
2009 ◽  
Vol 114 (13) ◽  
pp. 2575-2580 ◽  
Author(s):  
Mikkael A. Sekeres ◽  
David P. Steensma
Keyword(s):  
Clinical Trial ◽  
Disease Progression ◽  
Myelodysplastic Syndromes ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Refractory Disease ◽  
Prior Therapy ◽  
Previously Treated ◽  
New Challenges ◽  
Design Challenges

Abstract The recent approval of 3 drugs for the treatment of myelodysplastic syndromes (MDSs) has resulted in a revolution in therapeutic options that was absent a decade ago. At the same time, the changing MDS environment is raising new challenges in clinical trial design and defining new indications for MDS drugs. Many current trials still rely on IPSS-based enrollment criteria, despite the well-recognized limitations of the IPSS. Clinical trialists designing studies struggle with several important trial design challenges, including which patients constitute the “previously treated” and “relapsed/refractory” MDS populations, and how specifically to define disease “progression.” This article considers some of these issues as they relate to study design, including how to identify certain MDS populations and define disease progression.

scholarly journals A three‐dimensional neuroimaging based deep learning model to predict Alzheimer’s disease progression for clinical trial design

2021 ◽  
Vol 17 (S1) ◽  
Author(s):  
Scott Gladstein ◽  
Liuqing Yang ◽  
Dustin W Wooten ◽  
Hana Florian ◽  
Xin Huang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Deep Learning ◽  
Disease Progression ◽  
Trial Design ◽  
Three Dimensional ◽  
Clinical Trial Design ◽  
Learning Model ◽  
Deep Learning Model
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