The Importance of Categorizing and Analyzing Screen Failures when Recruiting Clinical Trial Participants
As science and technology advance, the demand for added clinical trials and more in-depth research increases. As a successful Contract Research Organization, TRI is known for contributing to the success and safety of clinical trials across multiple public and private health research sponsors. Among the services provided are recruitment, data collection, maintenance, and analytics. The ability to recruit eligible participants is one of multiple components that contribute to the success of a clinical trial, impacting study timeline, resources, and cost, as well as the participant/patient experience. To determine who is eligible to participate in a study, a set of parameters must be put in place within the protocol. The screening procedures and eligibility criteria depend on the targeted drug and treatment method. Eligibility decisions are based on variables such as current diagnosis, lab values, medical history, medications, or other qualifying factors.
Eligibility is determined through the pre-screening process, which can involve a chart review, survey, clinic visit, or telephone call. Once potential participants are contacted and decide to sign informed consent, they may still decline participation or be deemed ineligible to enroll. Screen failures are potential study participants who do not fit the parameters or eligibility criteria set by the protocol. If the eligibility criteria are not met, participants cannot move further in the study to begin intervention. In turn, a study cannot successfully move to the next stages of follow up and analysis unless a minimum number of participants complete intervention.
Recruitment is tracked throughout the trial to determine the percentage of study participants successfully enrolled, out of the total number prescreened and then screened. This should be done at the site level as well as for the study overall. In a study manuscript, the numbers of prescreened/screened/enrolled should be shown as the initial tiers in a standard consort diagram, along with the top few reasons for non-enrollment. Various participant characteristics and study parameters may prevent successful recruitment. Examples include investigational agents known or thought to interact with other drugs, which may require exclusion of participants taking specific ‘concomitant’ medications.
The trial may also be focused on a measure of frequency or severity of disease symptoms, such as minimum number of migraine headaches per week, or minimum score on a pain scale. Other factors are minimum or maximum age limits, gender, and exclusion of particular co-morbidities. Studies with frequent clinic visits or blood draws may deter those who may not be willing or able to comply. In one Phase 1 clinical study across 38 Phase 1 trials, 190 participants (24.6%) were screen failures out of 773 consents given.1 This was cited to be mostly due to lab results out of protocol-specified range for chemistry or the development of an interval medical issue. Screen failure rates also increased when patients had longer travel distances to participate in the study, showing that logistics and time commitments can also impact the enrollment results.1 When the number of screen failures is excessive for meeting the recruitment goals, study timeline, and/or budget, analysis of screen failures and attempting to limit such occurrences can be important tools for getting back on track and critical to success of a clinical trial.
The study design should include a mechanism for compiling the screening data from participating sites, categorizing failure reasons based on the eligibility criteria, and analyzing the reasons recruitment may not be on target. If adequate data are collected, TRI can provide real time analytics based on categorizing screen failures, producing frequency tables, and providing input to the client to improve recruitment and study success. For an oncology project on which we support recruitment, TRI maintains the client’s screening and enrollment data repository. Study staff enter their site’s pre-screen, screening, and recruitment progression data for each protocol, including why enrollment criteria were not met. Barriers in these trials have included the subject’s perception of research, opting for standard of care instead of investigational therapy, visit scheduling issues, coexisting conditions, receipt of prior therapies, and timing of required surgery. TRI analyzes these data monthly to identify patterns in recruitment barriers by site and protocol. Trend reports are provided to the sponsor, facilitating discussion and insight as to why recruitment may not be on target.
When barrier trends are identified, the study team or sponsor may choose to make changes to resolve the issues or take no action. For example, it may be justifiable to amend the protocol to expand eligibility criteria, such as age range or permissible lab chemistry values, or to reduce clinic visits, blood draws, or other study burdens to allow for increased recruitment. Alternatively, screen failure analysis may lead to changing the site mix by adding prequalified backup study sites, closing non-productive ones, or opening new sites to recruitment. With intelligent categorization and analysis of screen failures, effective study modifications can be made to improve recruitment outcomes for the current ongoing trial. Understanding screen failures may also inform future study designs and site selection for the same study drug or intervention. Screen failure categorization across disease-specific clinical trials can be applied to improve study design for future trials on the same disease or across many treatment areas, leading to improved recruitment methods and site selection.
- Mckane, A., Sima, C., Ramanathan, R. K., Jameson, G., Mast, C., White, E., … Weiss, G. J. (2013, June). Determinants of patient screen failures in Phase 1 clinical trials. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/23135779
About the Author
Casey Deans is a an In-House CRA with several years of experience in oncology clinical trial recruitment analytics and support.