Clinical Trial Design & Management: Best Practices for Small Budget Studies


ClinCapture hosts a panel discussion with experts from CROs and sponsor companies to discuss conducting quality clinical trials in a cost effective manner.

ClinCapture hosted the 12th BioTalks in Park City, UT, on the topic of Clinical Trial Design & Management: Best Practices for Small Budget Studies. This was the first event in the series hosted in the biotech hub of Salt Lake City. The panel discussion gathered experts from CROs and sponsor companies to conduct quality clinical trials in a cost effective manner.


Industry experts shared their knowledge on running high quality, small budget clinical trials covering clinical operations to clinical data management and biostatistics. The panel was moderated by Eric Morrie, Director of Product Operations at ClinCapture. He is managing customer support, professional services, and the product roadmap.


Adopting the Right Technology and Standards from The Ground Up

  Susan Hendrix, President of Pentara Corporation, is a statistician by training and has been in clinical trials for over 25 years managing study designs, statistical programming and clinical data management. In 2008, she started a specialty CRO that focused on Phase I research with a specialty in Alzheimer’s clinical studies. She claims that the biggest issue is how slowly the industry is adopting technology and standards. “We have been talking about CDISC since 1992” she claims, “and we’re still hesitant about it”.   “In terms of technology and standards, why is everyone rushing to try and get their study out there first rather than actually putting the time in to do it right the first time?” asks Jon Ward, Chief Executive Officer at Aspen Clinical Research. He has over 25 years of experience in health care and pharmaceuticals. He specialized in rare indications. Ward was drawn to clinical research in 2006 while working for nutraceutical clients and now covers both Phase I as well as larger studies across multiple indications. He further explains: “I’ve seen studies fail because the data management wasn’t really cleaning things up. They didn’t know they had 10,000 queries until the end and had to redo the whole study.”   Bernie LaSalle is the Director of Operations for the Biomedical Informatics Core (BMIC), Center for Clinical and Translational Science, at University of Utah. “Sometimes you can go back and fix the data but you have to spend almost as much time as you spent during your trial cleaning it up, getting it into the right format, and getting it to a place where you can analyze it. But sometimes you can’t go back and fix things if they were done wrong in the first place.” LaSalle has been in clinical research informatics since 1986, supporting everything from the Grant Fund and Single Investigator Studies to multicenter sponsor clinical trials. He conducted extensive research in clinical trial workflow and process efficiency.  

Leveraging Standards to Reconcile Disparate Data

  According to Ward, “big data” means gathering and aggregating data from a number of different and disparate sources: EHR data, smartphone data, and more. The problem is to make the data meaningful: “We need to build standards together and solve the issues around data syntax: It’s the semantic part that is really the main problem to make sense of the data.”   “As a statistician” says Hendrix, “I want beautiful categories, I want nice checkboxes, I want numbers, but with a whole bunch of data sets, it is very painful to standardize data. Retrofitting data and trying to combine big data from several sources that haven’t been planned to be put together is a nightmare.” She continues, “CDISC standards are a great step forward as they force everything to be categorical and numerical. They allow for a lot of information to be collected in a very straight-forward way that is easy to analyze after the fact. But you have to set up standards upfront to save money and time. Doing it upfront is probably only 5% or 10% of the big cost of reconciling the data at the end.”  

How To Leverage Predictive Analytics and Adaptive Study Design

  Is there enough information to predict the outcome of a clinical trial’s success or failure, early on? Adaptive clinical trials became popular in the mid 1990s. These types of studies aim at making adjustments or changes to the clinical trial depending on the data that’s coming in. “I think that the short answer for most trials is that this doesn’t work” claims Ward. “The industry is not agile enough to use three months of trial data and make rapid shifts. I think the data exists but it isn’t collected in a way where there is a formal mechanism to re-adjust quickly.”   Morrie states that one of the biggest challenges for sponsors to adopt adaptive design is that the FDA is asking drug developers to define thresholds and pivotal points upfront in the protocol. This is very difficult to predict ahead of time with limited experience, historic data or information. “However, there are large sponsor companies that manage to shut down trials that are not helping patients and start other clinical trials that could be more effective.” Only a small minority of sponsors has this agility.   “The best simulations that I’ve seen done is when all the clinical data that comes from an earlier study is involved” explains Hendrix. “The statisticians are involved with the historic data for the disease area and you’re pulling all that together into likely scenarios”. According to Hendrix, the most popular kind of adaptive design right now is one that drops study arms. Starting with the five arms with two different treatments and a couple different doses each and a placebo. Further in the study, the sponsors would drop the arms that are not as likely to show an effect once they are at a point where they can really see what’s going on. “This type of adaptive connections can save a lot of money”.  

About The Silicon Valley BioTalks

With chapters in Silicon Valley, CA and Park City, UT, BioTalks has gathered over 1,000 clinical trials professionals and has featured over 50 panelists from leading life science companies including Roche, Abbott, Santen Pharmaceutical, Elan Pharmaceuticals and Johnson & Johnson. Visit

and join the

Silicon Valley BioTalks group on Linkedin

to receive updates on the next session of this event.   *This blog originally appeared on ClinCapture’s blog 6/22/16

© 2024 MJH Life Sciences

All rights reserved.