Boosting enrolment in clinical trials: validation of a regional network model

Distance from Home to Research Center: A Barrier to In-Person Visits but Not Treatment Adherence in a Stroke Trial

BACKGROUND AND PURPOSE

Clinical trials often seek to enroll patients from both urban and rural areas to safeguard the generalizability of results. However, maintaining contact with patients who live away from a recruitment site, including rural areas, can be challenging. In this research we examine the effect of distance between patient and study centers on treatment adherence and retention.

METHODS

Secondary analysis of 2,466 participants in the Insulin Resistance Intervention after Stroke trial who were enrolled from research sites in the United States. Driving distance between the zipcodes of patient's reported place of residence and the study center was calculated. Outcome measures were loss to follow-up, completion of annual in-person visits, adherence to preventive therapy, and adherence to study drug in the first 3 years of participation. Logistic regression models were used to adjust for confounders.

RESULTS

Distance from residence to research center was not associated with loss to follow-up, adherence to study drug, or adherence to preventive therapy (p > 0.05 for each). However, patients who lived farther from the research center (>120 miles), compared to patients who lived closer (<60 miles), were less likely to complete the second annual in-person visit (62 vs. 81%; adjusted OR 0.48; 95% CI 0.31-0.75) and third visit (53 vs. 75%; adjusted OR 0.44; 95% CI 0.29-0.67).

CONCLUSIONS

Distance between patient and study center was an independent predictor of missed in-person visits but not with adherence to study treatment or preventive care.

Real-Time Enrollment Dashboard For Multisite Clinical Trials

OBJECTIVE

Achieving patient recruitment goals are critical for the successful completion of a clinical trial. We designed and developed a web-based dashboard for assisting in the management of clinical trial screening and enrollment.

MATERIALS AND METHODS

We use the dashboard to assist in the management of two observational studies of community-acquired pneumonia. Clinical research associates and managers using the dashboard were surveyed to determine its effectiveness as compared with traditional direct communication.

RESULTS

The dashboard has been in use since it was first introduced in May of 2014. Of the 23 staff responding to the survey, 77% felt that it was easier or much easier to use the dashboard for communication than to use direct communication.

CONCLUSION

We have designed and implemented a visualization dashboard for managing multi-site clinical trial enrollment in two community acquired pneumonia studies. Information dashboards are a useful tool for clinical trial management. They can be used as a standalone trial information tool or included into a larger management system.

Feasibility of feature-based indexing, clustering, and search of clinical trials. A case study of breast cancer trials from ClinicalTrials.gov

BACKGROUND

When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space.

OBJECTIVES

This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes.

METHODS

We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively.

RESULTS

We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction.

CONCLUSIONS

It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency.