trial simulation
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2021 ◽  
pp. 1-20
Author(s):  
Michael R. Leippe ◽  
Amanda N. Bergold ◽  
Nikoleta Despodova ◽  
Christopher Gettings ◽  
Donna Eisenstadt

2021 ◽  
pp. 135-146
Author(s):  
Kris Ferguson ◽  
Alaa Abd-Elsayed

This chapter will detail the surgical recommendations and step-by-step approaches for both trial stimulation and permanent implantation of peripheral nerve stimulation to treat sacroiliac joint pain. It also provides a brief history of this procedure, which has evolved from being a procedure that required careful, open dissection of the target nerve to a sleek treatment requiring only image guidance and a minimally invasive percutaneous approach. A trial stimulation period typically ranges from 7 to 14 days; steps for the trial simulation procedure include preparation, target point identification, and lead insertion. Additional topics for the permanent implantation procedure include tunneling and pocket dissection.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhaoyi Chen ◽  
Hansi Zhang ◽  
Yi Guo ◽  
Thomas J. George ◽  
Mattia Prosperi ◽  
...  

AbstractIn this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer’s disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial’s study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials’ design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.


2020 ◽  
Vol 22 (5) ◽  
Author(s):  
Peijuan Zhu ◽  
Chyi-Hung Hsu ◽  
Chuanpu Hu ◽  
Peggy Wong ◽  
Sherwin K. B. Sy ◽  
...  

2020 ◽  
Author(s):  
Zhaoyi Chen ◽  
Hansi Zhang ◽  
Yi Guo ◽  
Thomas J George ◽  
Mattia Prosperi ◽  
...  

AbstractClinical trials are essential but often have high financial costs and long execution time. Trial simulation using real world data (RWD) could potentially provide insights on a treatment’s efficacy and safety before running a large-scale trial. In this work, we explored the feasibility of using RWD from a large clinical data research network to simulate a randomized controlled trial of Alzheimer’s disease considering two different scenarios: an one-arm simulation of the standard-of-care control arm; and a two-arm simulation comparing treatment safety between the intervention and control arms with proper patient matching algorithms. We followed original trial’s design and addressed some key questions, including how to translate trial criteria to database queries and establish measures of safety (i.e., serious adverse events) from RWD. Our simulation generated results comparable to the original trial, but also exposed gaps in both trial simulation methodology and the generalizability issue of clinical trials.


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