Clinical trial performance metrics are among the most powerful tools for assessing progress, decision making, and measuring the strength of evidence used in clinical trials. These metrics are vital for both sponsors and regulatory authorities to make informed decisions about continuing a clinical trial, modifying the trial protocol, selecting the sample size for statistical power, or terminating a study early.
These metrics clarify a trial’s performance expectations regarding patient recruitment and retention, which drives both cost-effectiveness and efficiency. This is called the “critical path” in the drug development industry (i.e., when a clinical trial starts and ends, how long it takes). As the number of clinical trials increase year after year, planning and monitoring is more important than ever.
What Are Clinical Trial Metrics?
Clinical trial performance metrics are the numbers used to measure the performance of a clinical research team. This includes site patient accrual, site staff compliance with study procedures, site accuracy in data entry, and site retention rates. Sites are measured based on these metrics so that researchers can determine whether they are being effective.
The Importance of Measuring Site Performance
Clinical research organizations (CROs) rely on clinical trial data to make decisions about effective sites. Sites that do not meet acceptable performance levels may be dropped from studies or added to the list of sites that are excluded from ongoing study selection processes. Before being considered for future work, a site must prove itself to be effective at managing the clinical trial.
How to Measure Site Performance
Site performance is generally measured by researchers on three core metrics: 1) patient enrollment rate, 2) data accuracy, and 3) retention/attrition rate. All three are critical for developing an accurate picture of whether your clinic is meeting its recruitment goals.
1. Patient Enrollment Rate
In the fast-paced world of clinical trials, speed is essential. The faster a site can enroll patients, the faster researchers can begin to capture meaningful data related to study hypotheses and move on to new research.
As such, it’s essential to understand how well your clinic is meeting enrollment targets. One useful measure is the patient enrollment rate, which is calculated by dividing the number of patients who have been enrolled at a particular site by the total number of expected study participants.
2. Data Accuracy
The quality of the data being submitted is crucial. Sites must have a series of checks in place, so researchers are receiving accurate data at the right time throughout the study. Data accuracy can be checked by reviewing site logs or various other factors that may help indicate foul play with how data is being entered.
If the site data are not being recorded accurately, then there may be a misunderstanding about site performance and what is working or not working within certain regions or with different patient populations.
3. Patient Retention/Attrition
Understanding what drives patient enrollment is critical in designing the right research study for a particular protocol and recruitment approach. Patient attrition is an important factor that must be considered when looking at the overall impact on the overall timeline of a study. If there are clear clinical reasons for patient drop-outs, then further risk mitigation may need to be put into place within certain regions or with different patient populations.
For instance, if the site has abnormally high rates of patient enrollment due to geographic location, then these exclusion criteria may need to be more expansive in order to ensure that sites within certain regions are not enrolled in the study.
Examples of Clinical Trial Metrics
Examples of clinical trial performance metrics include:
- The number of subjects who completed the study (and the number remaining in the study)
- The number of subjects who discontinued and why (e.g., withdrew consent, lost to follow-up, etc.)
- The number of screening failures and reasons for failure (e.g., inability to obtain informed consent; supply issues, pre-existing medical condition, etc.)
- The number of subjects who were randomized by site
- The number of subjects who were randomized by study group type (treatment/placebo)
- The number of qualified sites that entered the study as well as those that met the patient flow criteria
- The number of qualified subjects who were screened by qualifying condition and who entered the study without major protocol deviations (inclusion/exclusion criteria violations), similar to the number entering without major deviations in the safety population
- The measure of patient’s compliance with study procedures or requirements, including pill count, frequency of adverse events reporting, etc. This may also help to anticipate potential enrollment bottlenecks that may be encountered throughout the study
Final Notes
Clinical trial performance metrics, including patient enrolment, attrition, and data accuracy, are key endpoints for sponsors to monitor. As the process of patient enrollment becomes more rigorous and time-consuming, it is important to have a clear understanding of how performance metrics are defined, which ones are most valuable for each study, and the best way to track them throughout the course of a trial. Clinical study design and analysis tools, including adaptive designs and Bayesian methods, are also designed to accommodate the increasing complexity involved in patient enrolment.
Sponsors, CROs, and sites should work together to define the appropriate performance metrics for a given study or trial population. Sites will have their own unique needs based on location, patient volume, and so on. A clear understanding of site demographics can help sponsors prioritize which criteria are most important to measure at each study site.
