Development and Validation of a Clinical Trial Accrual Predictive Regression Model at a Single NCI-Designated Comprehensive Cancer Center

2016 ◽  
Vol 14 (5) ◽  
pp. 561-569 ◽  
Author(s):  
Wendy R. Tate ◽  
Lee D. Cranmer
Keyword(s):  
Clinical Trial ◽  
Regression Model ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Predictive Regression ◽  
Clinical Trial Accrual ◽  
Development And Validation

scholarly journals Unpacking Trial Offers and Low Accrual Rates: A Qualitative Analysis of Clinic Visits With Physicians and Patients Potentially Eligible for a Prostate Cancer Clinical Trial

2020 ◽  
Vol 16 (2) ◽  
pp. e124-e131
Author(s):  
Lauren M. Hamel ◽  
David W. Dougherty ◽  
Terrance L. Albrecht ◽  
Mark Wojda ◽  
Alice Jordan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Trial ◽  
Cancer Clinical Trial ◽  
Cancer Center ◽  
Contributing Factors ◽  
High Risk Prostate Cancer ◽  
Risk Prostate Cancer ◽  
Clinical Trial Accrual ◽  
Clinical Interactions

PURPOSE: Cancer clinical trial accrual rates are low, and information about contributing factors is needed. We examined video-recorded clinical interactions to identify circumstances under which patients potentially eligible for a trial at a major cancer center were offered a trial. METHODS: We conducted a qualitative directed content analysis of 62 recorded interactions with physicians (n = 13) and patients with intermediate- or high-risk prostate cancer (n = 43). Patients were screened and potentially eligible for a trial. We observed and coded the interactions in 3 steps: (1) classification of all interactions as explicit offer, offer pending, trial discussed/not offered, or trial not discussed; (2) in interactions with no explicit offer, classification of whether the cancer had progressed; (3) in interactions classified as progression but no trial offered, identification of factors discussed that may explain the lack of an offer. RESULTS: Of the 62 interactions, 29% were classified as explicit offer, 12% as offer pending, 18% as trial discussed/not offered, and 39% as trial not discussed. Of those with no offer, 57% included information that the cancer had not progressed. In 68% of the remaining interactions with patients whose cancer had progressed but did not receive an offer, reasons for the lack of offer were identified, but in 32%, no explanation was provided. CONCLUSION: Even in optimal circumstances, few patients were offered a trial, often because their cancer had not progressed. Findings support professional recommendations to broaden trial inclusion criteria. Findings suggest accrual rates should reflect the proportion of eligible patients who enroll.

Metastatic colorectal cancer (mCRC) patterns of care: Implications for clinical trial design

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 4079-4079 ◽  
Author(s):  
C. S. Denlinger ◽  
M. A. Collins ◽  
Y. Wong ◽  
S. Litwin ◽  
N. J. Meropol
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Trial Design ◽  
Clinical Trial Design ◽  
New Drugs ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Treatment Change ◽  
Therapy Regimen

4079 Background: New approaches have expanded options for patients (pts) with mCRC. To characterize current practice paradigms that might bear on clinical trial design, we analyzed decision-making and treatment patterns in pts treated at a Comprehensive Cancer Center since the introduction of cetuximab (CET), and bevacizumab (BV). Methods: A retrospective review of all pts diagnosed with mCRC between 3/1/04 and 8/28/06 treated at Fox Chase Cancer Center. Results: 160 pts were treated, with 157 pts receiving at least one therapy regimen by 10 attending oncologists. There were 350 changes in therapy with 246 (70%) including continuation of at least one prior drug (92 BV, 111 fluoropyrimidines, 43 other). The most common reasons for treatment change were toxicity (33%), progressive disease (PD) (29%), treatment breaks (15%), and metastasectomy (11%) ( Table ). PD was a more common cause for treatment discontinuation in later phases of treatment (18% initial regimen vs. 36% subsequent regimens, p=0.0002). 24% of pts treated with oxaliplatin (OX) discontinued due to neuropathy. Hypersensitivity caused discontinuation in 5% of pts with OX and 7% of pts with CET. Resection of metastases was undertaken in 38% of pts. 43% of these pts received neoadjuvant therapy, and 56% received adjuvant therapy. 30% of pts have died, 29% remain on active treatment, 28% are on a treatment break, 3% are on hospice, and 11% are lost to follow-up. Conclusions: PD is no longer the primary reason for change of therapy in pts with mCRC. Metastasectomy is common and OX neuropathy is often treatment-limiting. These findings have important implications for endpoint selection and design of clinical trials in mCRC. Future clinical trials in mCRC must recognize treatment complexities and capture key components of decision-making that may result in prolonged survival. Furthermore, treatment breaks represent a potential window for the evaluation of new drugs. [Table: see text] No significant financial relationships to disclose.

Applying quality improvement methodologies to decrease the time-to-activation of new clinical trials at an NCI-designated comprehensive cancer center.

2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 296-296
Author(s):  
Timothy J Brown ◽  
Erin Fenske Williams ◽  
Patrice Griffith ◽  
Asal Shoushtari Rahimi ◽  
Rhonda Oilepo ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Quality Improvement ◽  
Six Sigma ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Process Map ◽  
Decision Points ◽  
Improvement Methodologies ◽  
Six Sigma Methodology ◽  
Pass Through

296 Background: Initiating a new clinical trial is burdensome and complex. The time to activate a clinical trial can directly affect the ability to provide innovative, state-of-the-art care to patients. We sought to understand the process of activating an oncology clinical trial at a matrix National Cancer Institute-designated, comprehensive cancer center. Methods: A multidisciplinary team of stakeholders within the cancer center, university, and affiliate hospitals held a retreat to map out the process of activating a clinical trial from packet receipt to enrollment of the first patient. We applied classical QI and Six Sigma methodology to determine bottlenecks and redundancies in activating a clinical trial. During this process, particular attention was paid to time to pass through each step and perceived barriers and bottlenecks were identified through group discussions. The time to activation was measured from the day the trial packet was received until the time when the trial was open for enrollment. Results: The process map identified 66 steps with 12 decision points to activate a new clinical trial. The following two steps were instituted first: 1) allow parallel scientific committee and institutional review board (IRB) review and 2) allow the clinical research coordination committee to review protocols for feasibility and university interest separate from the IRB approval process. These changes resulted in a mean time-to-activation change from 194 days at baseline to 135 days after these changes were implemented. The committee continues to track the activation time and this frame work is used to identify additional improvement steps. Conclusions: By applying quality improvement methodologies and Six Sigma principles, we were able to redesign redundant aspects of the process of activating a clinical trial at a matrix comprehensive cancer center. This was associated with a reduction of time to activation of trials. More importantly, the process map provides a framework to maintain these gains and implement further changes.

scholarly journals Monitoring Twitter Conversations for Targeted Recruitment in Cancer Trials in LA County: Protocol for a Mixed-Methods Pilot Study (Preprint)

2018 ◽  
Author(s):  
Katja Reuter ◽  
Praveen Angyan ◽  
NamQuyen Le ◽  
Alicia MacLennan ◽  
Sarah Cole ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Social Media ◽  
Clinical Trials ◽  
Pilot Study ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Translational Science ◽  
The Social ◽  
Media Intervention ◽  
Twitter Users

BACKGROUND Insufficient recruitment of participants remains a critical roadblock to successful clinical research, in particular clinical trials. Social media (SM) provides new ways for connecting potential participants with research opportunities. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues and increasing enrollment in cancer clinical trials. However, there is a lack of evidence that Twitter offers practical utility and impact. OBJECTIVE The objective of this pilot study is to examine the feasibility and impact of using Twitter monitoring data (i.e., user activity and their conversations about cancer-related conditions and concerns expressed by Twitter users in LA County) as a tool for enhancing clinical trial recruitment at a comprehensive cancer center. METHODS We will conduct a mixed-methods interrupted time series study design with a before and after SM recruitment intervention. Based on a preliminary analysis of eligible trials, we plan to onboard at least 84 clinical trials across six disease categories: breast cancer, colon cancer, kidney cancer, lymphoma, non-small cell lung cancer, and prostate cancer that are open to accrual at the USC Norris Comprehensive Cancer Center (USC Norris). We will monitor messages about the six cancer conditions posted by Twitter users in LA County. Recruitment for the trials will occur through the Twitter account (@USCTrials). Primary study outcomes include, first, feasibility and acceptance of the social media intervention among targeted Twitter users and the study teams of the onboarded trials, which will be assessed using qualitative interviews and 4-point Likert scale, and calculating the proportion of targeted Twitter users who engaged with outreach messages. Second, impact of the social media intervention will be measured by calculating the proportion of people who enrolled in trials. The enrollment rate will be compared between the active intervention period and the prior 10 months as historical control for each disease trial group. RESULTS This study has been funded by the National Center for Advancing Translational Science (NCATS) through a Clinical and Translational Science Award (CTSA) award. Study approval was obtained from the Clinical Investigations Committee (CIC) at USC Norris and the Institutional Review Board (IRB) at USC. Recruitment on Twitter started in February 2018. Data collection will be completed in November 2018. CONCLUSIONS This pilot project will provide preliminary data and practical insight into the application of publicly available Twitter data to identify and recruit clinical trial participants center across six cancer disease types. We will shed light on the acceptance of the SM intervention among Twitter users and study team members of the onboarded trials. If successful, the findings will inform a multisite, randomized controlled trial to determine the efficacy of the social media intervention across different locations and populations.

Improving the Time to Activation of New Clinical Trials at a National Cancer Institute–Designated Comprehensive Cancer Center

2020 ◽  
Vol 16 (4) ◽  
pp. e324-e332 ◽  
Author(s):  
Erin Williams ◽  
Timothy J. Brown ◽  
Patrice Griffith ◽  
Asal Rahimi ◽  
Rhonda Oilepo ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Quality Improvement ◽  
Clinical Research ◽  
National Cancer Institute ◽  
Six Sigma ◽  
Value Added ◽  
Scientific Committee ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Process Map

PURPOSE: The time it takes a performing site to activate a clinical trial can directly affect the ability to provide innovative and state-of-the-art care to patients. We sought to understand the process of activating an oncology clinical trial at a matrix National Cancer Institute–designated comprehensive cancer center. METHODS: A multidisciplinary team of stakeholders within the cancer center, university, and affiliate hospitals held a retreat to map out the process of activating a clinical trial. We applied classical quality improvement and Six Sigma methodology to determine bottlenecks and non–value-added time in activating a clinical trial. During this process, attention was paid to time to pass through each step, and perceived barriers and bottlenecks were identified through group discussions. RESULTS: The process map identified 66 steps with 12 decision points to activate a new clinical trial. The following two steps were instituted first: allow parallel scientific committee and institutional review board (IRB) review and allow the clinical research coordination committee, a group that determines university interest and feasibility, to review protocols independent of the IRB and scientific committee approval. The clinical research coordination committee continues to track the activation time, and this framework is used to identify additional improvement steps. CONCLUSION: By applying quality improvement methodologies and Six Sigma principles, we were able to identify redundancies in the process to activate a clinical trial. This allowed us to redesign the process of activating a clinical trial at a matrix comprehensive cancer center. More importantly, the process map provides a framework to maintain these gains and implement additional changes and serves as an example to deploy across the campus and at other similar institutions.

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