Dataset Information

Quantifying and visualizing site performance in clinical trials.

ABSTRACT: Background:One of the keys to running a successful clinical trial is the selection of high quality clinical sites, i.e., sites that are able to enroll patients quickly, engage them on an ongoing basis to prevent drop-out, and execute the trial in strict accordance to the clinical protocol. Intuitively, the historical track record of a site is one of the strongest predictors of its future performance; however, issues such as data availability and wide differences in protocol complexity can complicate interpretation. Here, we demonstrate how operational data derived from central laboratory services can provide key insights into the performance of clinical sites and help guide operational planning and site selection for new clinical trials. Methods:Our methodology uses the metadata associated with laboratory kit shipments to clinical sites (such as trial and anonymized patient identifiers, investigator names and addresses, sample collection and shipment dates, etc.) to reconstruct the complete schedule of patient visits and derive insights about the operational performance of those sites, including screening, enrollment, and drop-out rates and other quality indicators. This information can be displayed in its raw form or normalized to enable direct comparison of site performance across studies of varied design and complexity. Results:Leveraging Covance's market leadership in central laboratory services, we have assembled a database of operational metrics that spans more than 14,000 protocols, 1400 indications, 230,000 unique investigators, and 23 million patient visits and represents a significant fraction of all clinical trials run globally in the last few years. By analyzing this historical data, we are able to assess and compare the performance of clinical investigators across a wide range of therapeutic areas and study designs. This information can be aggregated across trials and geographies to gain further insights into country and regional trends, sometimes with surprising results. Conclusions:The use of operational data from Covance Central Laboratories provides a unique perspective into the performance of clinical sites with respect to many important metrics such as patient enrollment and retention. These metrics can, in turn, be used to guide operational planning and site selection for new clinical trials, thereby accelerating recruitment, improving quality, and reducing cost.

SUBMITTER: Yang E

PROVIDER: S-EPMC5898563 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Quantifying and visualizing site performance in clinical trials.

Yang Eric E O'Donovan Christopher C Phillips JodiLyn J Atkinson Leone L Ghosh Krishnendu K Agrafiotis Dimitris K DK

Contemporary clinical trials communications 20180131

<h4>Background</h4>One of the keys to running a successful clinical trial is the selection of high quality clinical sites, i.e., sites that are able to enroll patients quickly, engage them on an ongoing basis to prevent drop-out, and execute the trial in strict accordance to the clinical protocol. Intuitively, the historical track record of a site is one of the strongest predictors of its future performance; however, issues such as data availability and wide differences in protocol complexity ca ...[more]

PMID: 29696232

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Project description:ImportanceRandomized clinical trials (RCTs) are critical in advancing patient care, yet conducting such large-scale trials requires tremendous resources and coordination. Clinical site start-up performance metrics can provide insight into opportunities for improved trial efficiency but have not been well described.ObjectiveTo measure the start-up time needed to reach prespecified milestones across sites in large cardiovascular RCTs in North America and to evaluate how these metrics vary by time and type of regulatory review process.Design, setting, and participantsThis cohort study evaluated cardiovascular RCTs conducted from July 13, 2004, to February 1, 2017. The RCTs were coordinated by a single academic research organization, the Duke Clinical Research Institute. Nine consecutive trials with completed enrollment and publication of results in their target journal were studied. Data were analyzed from December 4, 2019, to January 11, 2021.ExposuresYear of trial enrollment initiation (2004-2007 vs 2008-2012) and use of a central vs local institutional review board (IRB).Main outcomes and measuresThe primary outcome was the median start-up time (from study protocol delivery to first participant enrollment) as compared by trial year and type of IRB used. The median start-up time for the top 10% of sites was also reported. Secondary outcomes included time to site regulatory approval, time to contract execution, and time to site activation.ResultsFor the 9 RCTs included, the median site start-up time shortened only slightly over time from 267 days (interquartile range [IQR], 185-358 days) for 2004-2007 trials to 237 days (IQR, 162-343 days) for 2008-2012 trials (overall median, 255 days [IQR, 177-350 days]; P < .001). For the top 10% of sites, median start-up time was 107 days (IQR, 95-121 days) for 2004-2007 trials vs 104 days (IQR, 84-118 days) for 2008-2012 trials (overall median, 106 days [IQR, 90-120 days]; P = .04). The median start-up time was shorter among sites using a central IRB (199 days [IQR, 140-292 days]) than those using a local IRB (287 days [IQR, 205-390 days]; P < .001).Conclusions and relevanceThis cohort study of North American research sites in large cardiovascular RCTs found a duration of nearly 9 months from the time of study protocol delivery to the first participant enrollment; this metric was only slightly shortened during the study period but was reduced to less than 4 months for top-performing sites. These findings suggest that the use of central IRBs has the potential to improve RCT efficiency.

Project description:BackgroundGraphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis.MethodsWe propose the application of stacked CA using contribution biplots as a tool to explore differences in AE data among treatments in clinical trials. We defined five levels of refinement for the analysis based on data derived from the Common Terminology Criteria for Adverse Events (CTCAE) grades, domains, terms and their combinations. In addition, we developed a Shiny app built in an R-package, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 factorial trial comparing radiation therapy with either capecitabine (Cape) or 5-fluorouracil (5-FU) alone with or without oxaliplatin (Oxa), and NSABP B-35, a double-blind RCT comparing tamoxifen to anastrozole in postmenopausal women with hormone-positive ductal carcinoma in situ.ResultsIn the R04 trial (n = 1308), CA biplots displayed the discrepancies between single agent treatments and their combinations with Oxa at all levels of AE classes, such that these discrepancies were responsible for the largest portion of the explained variability among treatments. In addition, an interaction effect when adding Oxa to Cape/5-FU was identified when the distance between Cape+Oxa and 5-FU + Oxa was observed to be larger than the distance between 5-FU and Cape, with Cape+Oxa and 5-FU + Oxa in different quadrants of the CA biplots. In the B35 trial (n = 3009), CA biplots showed different patterns for non-adherent Anastrozole and Tamoxifen compared with their adherent counterparts.ConclusionCA with contribution biplot is an effective tool that can be used to summarize AE data in a two-dimensional display while minimizing the loss of information and interpretation.

Project description:BackgroundThe urgency of the climate crisis requires attention from biomedical research, not least clinical trials which can involve significant greenhouse gas emissions. The Low Carbon Clinical Trials Working Group set out a strategy to reduce the emissions of clinical trials, starting with the development of a method to measure their carbon footprint (CO2e).MethodsAs a first step, we developed a process map defining clinical trial core activities. Corresponding emission factors were sourced to convert activity data into greenhouse gas emissions. The subsequent method was applied to two Cancer Research UK (CRUK)-funded trials (the international randomised sarcoma trial CASPS (ISRCTN63733470) and the UK cohort-based breast cancer trial PRIMETIME (ISRCTN41579286)). A guidance document defining the scope, method and assumptions was written to allow application to any publicly funded/investigator initiated clinical trial.ResultsTrial specific activities over and above routine care were grouped into 10 modules covering trial set up, conduct and closure. We identified emission factors for all trial activities within both trials and used them to estimate their total carbon footprint. The carbon footprint of CASPS, an international phase 2 trial of an investigational medicinal product with 47 participants, was 72 tonnes CO2e, largely attributable to clinical trials unit emissions and staff travel. PRIMETIME, a UK-based phase 3 non-investigational medicinal product trial with 1962 patients, produced 89 tonnes CO2e, largely attributable to trial-specific in-person participant assessments.ConclusionWe have developed a method and guidance that trialists can use to determine the carbon footprint of clinical trials. The guidance can be used to identify carbon hotspots where alternative approaches to trial design and conduct could reduce a trial footprint, and where methodology research is required to investigate the potential impact of interventions taken to reduce carbon emissions. We will continue to refine the guidance to increase the potential application and improve usability.

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Quantifying and visualizing site performance in clinical trials.

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Quantifying and visualizing site performance in clinical trials.

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