Trial Design and Methodology in Nitrate Investigations

2015 ◽  
pp. 87-92
Author(s):  
Felix Burkart
Keyword(s):  
Trial Design

Author response for "Clinical trial risk in leukemia: Biomarkers and trial design"

2020 ◽  
Author(s):  
Alice J. Li ◽  
Jasper P. Dhanraj ◽  
Gilberto Lopes ◽  
Jayson L. Parker
Keyword(s):  
Clinical Trial ◽  
Trial Design ◽  
Author Response

Rationale and trial design of the Left Atrial Appendage Occlusion Study (LAAOS III)

2014 ◽  
Vol 62 (S 01) ◽  
Author(s):  
W. Reents ◽  
A. Diegeler ◽  
J. Babin-Ebell ◽  
A. Böning ◽  
R.P. Whitlock
Keyword(s):  
Left Atrial ◽  
Trial Design ◽  
Left Atrial Appendage ◽  
Atrial Appendage ◽  
Left Atrial Appendage Occlusion

Integrating radiomics into clinical trial design

2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Jessica J. Waninger ◽  
Michael D. Green ◽  
Catherine Cheze Le Rest ◽  
Benjamin Rosen ◽  
Issam El Naqa
Keyword(s):  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design

Avenues to Precision Oncology

2020 ◽  
pp. 36-45
Author(s):  
Alexander Meisel
Keyword(s):  
Precision Medicine ◽  
Trial Design ◽  
Ad Hoc ◽  
Molecular Targets ◽  
Clinical Trial Design ◽  
Predictive Biomarkers ◽  
Precision Oncology ◽  
Companion Diagnostics ◽  
Bona Fide ◽  
The One

Until recently, the clinical management of cancer heavily relied on anatomical and histopathological criteria, with ad hoc guidelines directing the therapeutic choices in specific indications. In the last years, the development and therapeutic implementation of novel anticancer therapies significantly improved the clinical outcome of cancer patients. Nonetheless, such cutting-edge approaches revealed the limitation of the one-size-fits-all paradigm. The newly discovered molecular targets can be exploited either as bona fide targets for subsequent drug development, or as tools to precision medicine, in the form of prognostic and/or predictive biomarkers. This article provides an overview of some of the most recent advances in precision medicine in oncology, with a focus on novel tissue-agnostic anticancer therapies. The definition and implementation of biomarkers and companion diagnostics in clinical trials and clinical practice are also discussed, as well as the changing landscape in clinical trial design.

scholarly journals Improving pragmatic clinical trial design using real-world data

2019 ◽  
Vol 16 (3) ◽  
pp. 273-282 ◽  
Author(s):  
Susan M Shortreed ◽  
Carolyn M Rutter ◽  
Andrea J Cook ◽  
Gregory E Simon
Keyword(s):  
Clinical Trials ◽  
Trial Design ◽  
Suicide Attempts ◽  
Real World Data ◽  
Electronic Health Record Data ◽  
Pragmatic Clinical Trial ◽  
Pragmatic Clinical Trials ◽  
Sample Size Calculations ◽  
Record Data ◽  
Electronic Health

Background Pragmatic clinical trials often use automated data sources such as electronic health records, claims, or registries to identify eligible individuals and collect outcome information. A specific advantage that this automated data collection often yields is having data on potential participants when design decisions are being made. We outline how this data can be used to inform trial design. Methods Our work is motivated by a pragmatic clinical trial evaluating the impact of suicide-prevention outreach interventions on fatal and non-fatal suicide attempts in the 18 months after randomization. We illustrate our recommended approaches for designing pragmatic clinical trials using historical data from the health systems participating in this study. Specifically, we illustrate how electronic health record data can be used to inform the selection of trial eligibility requirements, to estimate the distribution of participant characteristics over the course of the trial, and to conduct power and sample size calculations. Results Data from 122,873 people with patient health questionnaire (PHQ) responses, recorded in their electronic health records between 1 July 2010 and 31 March 2012, were used to show that the suicide attempt rate in the 18 months following completion of the questionnaire varies by response to item nine of the PHQ. We estimated that the proportion of individuals with a prior recorded elevated PHQ (i.e. history of suicidal ideation) would decrease from approximately 50% at the beginning of a trial to about 5%, 50 weeks later. Using electronic health record data, we conducted simulations to estimate the power to detect a 25% reduction in suicide attempts. Simulation-based power calculations estimated that randomizing 8000 participants per randomization arm would allow 90% power to detect a 25% reduction in the suicide attempt rate in the intervention arm compared to usual care at an alpha rate of 0.05. Conclusions Historical data can be used to inform the design of pragmatic clinical trials, a strength of trials that use automated data collection for randomizing participants and assessing outcomes. In particular, realistic sample size calculations can be conducted using real-world data from the health systems in which the trial will be conducted. Data-informed trial design should yield more realistic estimates of statistical power and maximize efficiency of trial recruitment.

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