Medidata Solutions

https://www.appliedclinicaltrialsonline.com/act_w/data_management

The types and sources of clinical trial data streams are increasing dramatically in both volume and variability along with decentralized trial designs. The combination of increasingly complex protocols and expanded clinical data streams have created the perfect storm to accelerate transformation in data management. In this ‘D-Clinical Age’, leveraging modern technologies and new approaches and mind sets is required to accelerate the development of new treatments. This webinar will examine how data management can drive digital transformation with the right mix of technology, skills and process redesign.

Bring trials to patients today by enabling the collection of digital streams from all sources

Discover new technology and processes needed to speed drugs to market

Learn how in this new age of D-Clinical, data management can transform clinical trials

Katrina Rice, Chief Delivery Officer, eClinical Solutions

Ted Kirby, Senior Director, Product Marketing, Medidata Solutions

Jaclyn Dye, Senior Director, Head of Clinical Data Management, Deciphera Pharmaceuticals

Thursday, December 10, 2020 at 11am EST | 8am PST | 4pm GMT | 5pm CET

 Medidata Solutions | Dassult Systems | eClinical Solutions

Webcasts

***Live: December 9, 2020, at 11am EST| 8am PST| 4pm GMT| 5pm CET*** In this ‘D-Clinical Age’, leveraging modern technologies and new approaches and mind sets is required to accelerate the development of new treatments. This webinar will examine how data management can drive digital transformation with the right mix of technology, skills and process redesign.***On demand available after airing until Dec. 9, 2021.***


Modernizing Data Management in the D-Clinical Age

	Quite often, during the course of a clinical trial, the need arises to update case report forms (CRFs). In the world of electronic data capture, these forms are electronic, or “eCRFs.” Such updates can arise from protocol amendments, changes to the eCRF specification requirements or to the logic that automates the entry or verification of data in the study. Creating a new version of the eCRF and migrating subject records to that version is time consuming and costly, and can add additional burden-not only to sponsor and CRO personnel but also to sites. It is therefore imperative to keep such eCRF updates and migrations to the necessary minimum. 

	With this in mind, we took a look at how eCRF design complexity correlates with the number of migrations performed while a study is actively collecting subject data. Our expectation was that a correlation exists, and this was confirmed by the data. As you can see in the graph, the more complex the eCRF design the more likely it was to require an update during the course of the study. The eCRF design complexity is based on the number of unique eCRF pages, edit checks (custom and standard) and derivations included in the design. The studies included in the dataset were grouped based on their eCRF design complexity and the average number of migrations performed per study. Our findings indicated that the studies with the highest eCRF complexity also performed more migrations, which results in more time spent maintaining the eCRFs.    

	The study-planning phase should take into account factors that will affect eCRF design efficiency.  Having a lower eCRF design complexity will have a positive downstream effect by mitigating the need for migrations and lessening burden on study teams and site staff during the course of the study.

Articles

Medidata took a look at how eCRF design complexity correlates with the number of migrations performed while a study is actively collecting subject data.

Time for Another CRF Migration

As the industry is coming to know, risk-based monitoring can significantly reduce study monitoring costs through the promise of reducing on-site visits. Over the past few years, there has been a downward trend in the number of visit days per year, per site across the clinical trial landscape, as supported by data pulled from the Medidata Insights

Based on this trend, some might assume there would also be a similar reduction in the query open rate of site monitors coupled with a corresponding uptick in the number of data manager queries—indicating possible “compensation” for mistakes/inconsistencies not captured by monitors.

However, based on the analysis depicted in Figure 1, the number of site monitor and data manager queries has remained relatively flat since 2010. Conversely, there was an increase in the rate of auto queries—those generated programmatically by the electronic data capture (EDC) system in response to electronic case report form (eCRF) data entries that violate rules/boundary conditions. Auto queries are generated at the time of data entry and flag many of the data errors that would otherwise be manually detected by site monitors or data managers. Since the per-query cost for auto queries is significantly lower than that for manual queries, using edit check/auto query technology delivers a more attractive return on investment (ROI) than manual queries. Further, the analysis revealed a 16% increase in the frequency of auto queries over the three-year period that inversely corresponds to a 28% decrease in the site visit rate. 

Based on this analysis of the data, we conclude that the higher rate of auto queries is not specifically related to the decrease in on-site monitoring, but rather a result of increased adoption of the edit checking technology inherent in most EDC systems. This is a positive sign that the industry is maturing in its use of EDC and realizing the tremendous value this capability offers toward ensuring data quality. The results may also indicate that site monitors are becoming more efficient since their query rates remain level for both on- and off-site queries, even though they’re spending fewer days at sites year after year. So even though sites may not be seeing their site monitors as frequently, they can rest assured knowing their query rate remains the same!

Have You Seen Your Site Monitor Lately?

The quality and ultimate reliability of clinical data collected from investigative sites during a study depends on many factors. One area that may not get enough attention is the cycle time from subject visit through eCRF data capture, and final review and cleaning of that data. Site personnel's transcription of source data generated during subject visits into the sponsor-provided eCRF system can cause long delays. These delays allow loss of memory to potentially impact proper interpretation of information originally entered into the patient chart. Once the eCRF data has been captured, further delays in review and querying of that data by sponsor personnel (e.g., data managers and site monitors) will only exacerbate this issue. 

The Medidata Insights metrics warehouse includes a number of metrics focused on these data capture and cleaning cycle times. The graph shows the industry trend for two of these cycle times in particular: 					

Subject visit through final closure of queries related to that visit (i.e., overall data capture through cleaning cycle time)

eCRF entry through generation of data manager queries

This month's graph presents the industry median values for Phase II and III studies from 2009 through 2012. The overall data capture and cleaning cycle time efficiency does show a positive downward trend over this period—from 36 days in 2009 to 33 days in 2012. It actually dipped below 30 days in 2011 before rising again. This certainly supports the notion that organizations have been putting increased focus on these processes in recent years, and in particular we're noticing a positive downward trend in the time from subject visit to eCRF entry by sites. 

The trend in time from eCRF entry to generation of data manager queries is very interesting since it correlates closely with the overall capture and cleaning cycle time trend. In particular, it appears to be trending downward in the years prior to 2012 and then back up again in 2012. While not yet conclusive, the 2012 "bounce" in this metric is a very likely driver of the similar bounce upward in the overall cycle time metric. What continues to amaze is the actual length of that cycle time—right-hand axis on the graph—where the annual benchmark for the industry ranges from 59 to 89 days. This amount of lag time in generation of queries to the site from data management personnel cannot be looked at as having a positive influence on data quality.  

This brings up an ongoing—and sometimes heated—debate we have seen in clinical organizations regarding the proper cadence of sponsor team data reviews. There were essentially two distinct schools of thought—one that insisted data managers wait until site monitors have completed their source document verification (SDV) review of the eCRF data before conducting their reviews, and the other that insisted data managers should conduct their reviews as soon as the data was available in the eCRF system regardless of the status of site monitor review. It is apparent from the current industry benchmark that most organizations are still tied to the "monitors first" approach. 

We believe that parallel data reviews are the best way to accomplish this. What do you think? 

Data Cleaning Cycle Times Are Improving, But There's Still A Long Way to Go

The trend in time from eCRF entry to generation of data manager queries is very interesting since it correlates closely with the overall capture and cleaning cycle time trend. In particular, it appears to be trending downward in the years prior to 2012 and then back up again in 2012. While not yet conclusive, the 2012 "bounce" in this metric is a very likely driver of the similar bounce upward in the overall cycle time metric. What continues to amaze is the actual length of that cycle time—right-hand axis on the graph—where the annual benchmark for the industry ranges from 59 to 89 days. This amount of lag time in generation of queries to the site from data management personnel cannot be looked at as having a positive influence on data quality. 

We believe that parallel data reviews are the best way to accomplish this. What do you think?  

Data correction rates are indicative of the overall quality of data during a clinical trial. A higher rate of data correction would indicate a lower quality of data being entered, which raises concerns over the accuracy of data being collected during the study,and also, the level of training of the site personnel with electronic data capture (EDC) systems or other aspects of the protocol. The training and comfort level site personnel have with EDC systems would also contribute toward timely entry of data during the conduct of the study. A longer duration of time between subject visits to actual entry into the EDC would delay important decisions that determine the course of the study and may even indirectly impact patient safety. 

This month's Insights graph provides a comparative—and contextual—evaluation of trends in terms of data correction rates and the time from subject visit to eCRF entry. This data is derived from Phase II and III studies in the Medidata Insights metrics warehouse for the time period between 2007 and 2012. As evident from the graph, the data correction rate remains essentially flat while visit to data entry has a downward trend, with an almost 80% decrease during the time period.  This clearly indicates that timely data entry does not necessarily translate to high initial data accuracy. 

Interestingly, data correction rates also decreased 21% in 2012, from an all-time high at 6.6% in 2007.  It remains to be seen if this downward shift in 2012 would usher a downward trend in the years to come. It is possible that any shift in data correction rates due to timely entry of data (as suggested by the downward trend), is masked by other factors such as increases in the complexity of both eCRFs and protocols. A decreasing trend in data entry time is an indicator of better overall use of EDC systems, which can be used to relay insightful information, during the course of a clinical trial. It also highlights the potential opportunity of business analytics solutions, which seamlessly gather data from EDC and other eClinical systems to serve as key drivers for better planning and monitoring of clinical trials.  

What other factors do you think are impacting data correction rates? As always, Medidata is interested to hear your take on this result. Please stay tuned as the company delves further into the Insights metric data through 2012. 

Medidata Solutions

Articles

Modernizing Data Management in the D-Clinical Age

Time for Another CRF Migration

Have You Seen Your Site Monitor Lately?

Data Cleaning Cycle Times Are Improving, But There's Still A Long Way to Go

Data Cleaning Cycle Times Are Improving, But There's Still A Long Way to Go

In the Race to Capture Clinical Data, Does Data Quality Suffer?