Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies

Freely accessible software for recruitment prediction and recruitment monitoring of clinical trials: A systematic review

Background

The successful completion of clinical trials ultimately depends on realistic recruitment predictions. Statistical methods for recruitment prediction implemented in a free-of-charge open-source software could be routinely used by researchers worldwide to design clinical trials. However, the availability of such software implementations is currently unclear.

Methods

Two independent reviewers conducted a systematic review following PRISMA guidelines. Eligible articles included English publications focused on statistical methods for recruitment prediction and monitoring that referred to software implementations. The list of articles retrieved from well-established data bases was enriched by backtracking of references provided by eligible articles. The current software availability and open-source status were tabulated.

Results

We found 21 eligible articles, 7 of which (33 %) provide freely accessible software. Ultimately, only one article provides a link to an easy-to-comprehend, well-documented, and currently directly applicable free-of-charge open-source software. The lack of availability is mainly caused by blocked access and outdated links.

Conclusions

While several software implementations exist for recruitment prediction, only a small fraction is freely accessible. These results highlight the need for future efforts to achieve free access to well-documented software implementations supporting researchers in routinely using statistical methods to arrive at realistic recruitment predictions in clinical trials.

Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center

Introduction

Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance.

Methods

The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate.

Results

Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935.

Conclusions

This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

Impact of a Clinical Genomics Program on Trial Accrual for Targeted Treatments: Practical Approach Overcoming Barriers to Accrual for Underserved Patients

PURPOSE

Clinical trials of novel and targeted agents increasingly require biomarkers for eligibility. Precision oncology continues to evolve, but challenges hamper broad use of molecular profiling (MP) that could increase the number of patients benefiting from targeted therapy. We implemented an integrated clinical genomics program (CGP), including a virtual Molecular Tumor Board (MTB), and examined its impact on MP use and impact on clinical trial accrual in a multisite regional-based cancer system with an emphasis on effects for isolated clinicians.

METHODS

We assessed MP and MTB use from 2010 to 2020 by practice location, physician experience, and patient characteristics. Use of MTB-recommended treatments was assessed. Clinical trial enrollment was evaluated for patients with MP versus MP and MTB review.

RESULTS

After CGP implementation, the number of physicians using MP and the number of MP tests increased ≥ 10-fold. The proportion of Hispanic patients with MP was the same as that in the system (both 2%) with marginal differences observed in the proportion of African Americans tested compared with the system population (16% v 19%). Physicians followed MTB treatment recommendations in 74% of cases. Rapid clinical decline was the most common reason why physicians did not follow MTB recommendations. Clinical trial accrual was 15% (669 of 4,459) for patients with MP alone and 28% (94 of 334) with both MP and MTB review. Clinical trial availability and patient out-of-pocket costs affected MP use.

CONCLUSION

Integrating CGP into clinical workflow with decision support tools, trial matching, and management of patient costs led to increased use of MP by physicians with all levels of experience, enhanced clinical trial accrual, and has the potential to reduce disparities in MP.

Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)-rationale and design for an international collaborative study

BACKGROUND

Poor recruitment of patients is the predominant reason for early termination of randomized clinical trials (RCTs). Systematic empirical investigations and validation studies of existing recruitment models, however, are lacking. We aim to provide evidence-based guidance on how to predict and monitor recruitment of patients into RCTs. Our specific objectives are the following: (1) to establish a large sample of RCTs (target n = 300) with individual patient recruitment data from a large variety of RCTs, (2) to investigate participant recruitment patterns and study site recruitment patterns and their association with the overall recruitment process, (3) to investigate the validity of a freely available recruitment model, and (4) to develop a user-friendly tool to assist trial investigators in the planning and monitoring of the recruitment process.

METHODS

Eligible RCTs need to have completed the recruitment process, used a parallel group design, and investigated any healthcare intervention where participants had the free choice to participate. To establish the planned sample of RCTs, we will use our contacts to national and international RCT networks, clinical trial units, and individual trial investigators. From included RCTs, we will collect patient-level information (date of randomization), site-level information (date of trial site activation), and trial-level information (target sample size). We will examine recruitment patterns using recruitment trajectories and stratifications by RCT characteristics. We will investigate associations of early recruitment patterns with overall recruitment by correlation and multivariable regression. To examine the validity of a freely available Bayesian prediction model, we will compare model predictions to collected empirical data of included RCTs. Finally, we will user-test any promising tool using qualitative methods for further tool improvement.

DISCUSSION

This research will contribute to a better understanding of participant recruitment to RCTs, which could enhance efficiency and reduce the waste of resources in clinical research with a comprehensive, concerted, international effort.

Improving the efficiency of clinical trials by standardizing processes for Investigator Initiated Trials

Early phase clinical trials are the first step in testing new medications and therapeutics developed by clinical and biomedical investigators. These trials aim to find a safe dose of a newly targeted drug (phase I) or find out more about the side effects and early signals of treatment efficacy (phase II). In a research institute, many biomedical investigators in oncology are encouraged to initiate such trials early in their careers as part of developing their research portfolio. These investigator-initiated trials (IITs) are funded internally by the University of Kansas Cancer Center or partially funded by pharmaceutical companies. As financial, administrative, and practical considerations play an essential role in the successful completion of IITs, it is imperative to efficiently allocate resources to plan, design, and execute these studies within the allotted time. This manuscript describes monitoring tools and processes to improve the efficiency, cost-effectivness, and reliability of IITs. The contributions of this team to processes such as: participant recruitment, feasibility analysis, clinical trial design, accrual monitoring, data management, interim analysis support, and final analysis and reporting are described in detail. This manuscript elucidates how, through the aid of technology and dedicated personnel support, the efficiency of IIT-related processes can be improved. Early results of these initiatives look promising, and the Biostatistics and Informatics team intends to continue fostering innovative methodologies to enhance cancer research by improving the efficiency of IITs.

Bayesian accrual modeling and prediction in multicenter clinical trials with varying center activation times

Investigators who manage multicenter clinical trials need to pay careful attention to patterns of subject accrual, and the prediction of activation time for pending centers is potentially crucial for subject accrual prediction. We propose a Bayesian hierarchical model to predict subject accrual for multicenter clinical trials in which center activation times vary. We define center activation time as the time at which a center can begin enrolling patients in the trial. The difference in activation times between centers is assumed to follow an exponential distribution, and the model of subject accrual integrates prior information for the study with actual enrollment progress. We apply our proposed Bayesian multicenter accrual model to two multicenter clinical studies. The first is the PAIN-CONTRoLS study, a multicenter clinical trial with a goal of activating 40 centers and enrolling 400 patients within 104 weeks. The second is the HOBIT trial, a multicenter clinical trial with a goal of activating 14 centers and enrolling 200 subjects within 36 months. In summary, the Bayesian multicenter accrual model provides a prediction of subject accrual while accounting for both center- and individual patient-level variation.