Improving protocol design feasibility to drive drug development economics and performance

A Case Study Assessment on the Rationale for, and Relevance of, Non-Core Protocol Data

To better understand the nature of Non-Core procedures and derive new insight into protocol simplification and optimization, Tufts CSDD collaborated with the FDA and sponsor companies to assess alignment on the rationale for collecting, and relevance of, Non-Core protocol data. Twelve sponsor companies classified and rated 700 distinct procedures from 19 pivotal trials supporting new drug and biologics approvals. FDA reviewers classified and rated 80 distinct procedures for three of the 19 pivotal trials. The results of this assessment indicate areas of alignment and misalignment. Sponsors and FDA reviewers agreed on the classification for more than half of endpoints. However, FDA reviewers classified a much higher percentage of procedures as Non-Core (26% vs. 18%) with the largest proportion (50%) of these procedures perceived as Core by sponsor companies. Sponsors indicated that one-out-of-six Non-Core procedures were administered due to perceived regulatory requirement and expectation. The results of this study characterize the challenge in aligning the different-and potentially conflicting-imperatives of sponsors and regulators and speak to the importance of more effective FDA-sponsor communication to help simplify protocol designs.

Applying Systems Thinking to Inform Decentralized Clinical Trial Planning and Deployment

Recently, there has been a growing interest in understanding how decentralized clinical trial (DCT) solutions can mitigate existing challenges in clinical development, particularly participant burden and access, and the collection, management, and quality of clinical data. This paper examines DCT deployments, emphasizing how they are integrated and how they may impact clinical trial oversight, management, and execution. We propose a conceptual framework that employs systems thinking to evaluate the impact on key stakeholders through a reiterative assessment of pain points. We conclude that decentralized solutions should be customized to meet patient needs and preferences and the unique requirements of each clinical trial. We discuss how DCT elements introduce new demands and pressures within the existing system and reflect on enablers that can overcome DCT implementation challenges. As stakeholders look for ways to make clinical research more relevant and accessible to a larger and more diverse patient population, further robust and granular research is needed to quantify the impact of DCTs empirically.

Protocol Design and Performance Benchmarks by Phase and by Oncology and Rare Disease Subgroups

BACKGROUND

Benchmark data characterizing protocol design practices and performance informs clinical trial design decisions and serves as important baseline measures for assessing protocol design behaviors and their impact during and post-pandemic.

METHODS

Tufts CSDD, in collaboration with a working group of 20 major and mid-sized pharmaceutical companies and CROs, gathered phase I-III data from protocols completed just prior to the start of the global pandemic.

RESULTS

Data for 187 protocols were analyzed to derive benchmarks overall and for two primary subgroups: oncology vs. non-oncology protocols and rare disease vs. non-rare disease protocols. The results show a continuing upward trend across all protocol design variables. Phase II and III protocols average more endpoints, eligibility criteria, protocol pages; investigative sites; countries and datapoints collected. Oncology and rare disease protocols' enrolled-to-completion rates are much lower, involve a much higher average number of countries and investigative sites, require more planned patient visits and generate considerably more clinical research data. As such, oncology and rare disease clinical trial cycle times are longer-most notably at time periods occurring after study startup and prior to database lock-due to intense patient recruitment and retention challenges.

CONCLUSIONS

The results of this study present valuable design insights and comparative baseline measures. The implications of these results and the expected impact of decentralized clinical trials on protocol design practices and performance is discussed.

Changes in key recruitment performance metrics from 2008–2019 in industry-sponsored phase III clinical trials registered at ClinicalTrials.gov

Background

Increasing costs and complexity in clinical trials requires recruitment of more narrowly defined patient populations. However, recruitment for clinical trials remains a considerable challenge.

Aim

Our overall aim was to quantify recruitment performance in industry-sponsored phase III clinical trials conducted globally during 2008–2019 with primary aim to examine development of overall clinical trial measures (number of trials completed, number of participants enrolled, trial duration in months) and key recruitment metrics (recruitment rate, number of sites, number of patients enrolled per site).

Methods

The publicly available AACT database containing data on all trials registered at ClinicalTrials.gov since 2008 was used. The analysis was completed during three time periods from 2008–2019 of 4 years each.

Results and conclusion

Recruitment duration for industry-sponsored phase III clinical trials have increased significantly during the last 12 years from an average recruitment period of 13 months (IQR 7–23) in 2008–2011 to 18 months (IQR 11–28) in 2016–2019 (p = 0.0068). Further, phase III clinical trials have increased the number of registered sites per clinical trial by more than 30% during the last 12 years from a median number 43 sites (IQR 17–84) in 2012–2015 to 64 sites (IQR 30–118) in 2016–2019 (p = 0.025), and concurrently, the number of participants enrolled in clinical research has decreased significantly from 2012–2015 and 2016–2019 (p = 0.046). We believe that these findings indicate that recruitment for phase III clinical trials is less effective today compared to 12 years ago.

Machine Learning Prediction of Clinical Trial Operational Efficiency

Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration.

Review article: randomised controlled trials in inflammatory bowel disease-common challenges and potential solutions

BACKGROUND

Recruitment rates for Crohn's disease and ulcerative colitis clinical trials continue to decrease annually. The inability to reach recruitment targets and complete trials has serious implications for stakeholders in the inflammatory bowel disease (IBD) community. Action is required to ensure patients with an unmet medical need have access to new therapies to improve the management of their IBD.

AIMS

Identify challenges contributing to recruitment decline in IBD clinical trials and propose potential solutions.

METHODS

PubMed and Google were used to identify literature, regulatory guidelines and conference proceedings related to IBD clinical trials and related concepts. Data on IBD clinical trials conducted between 1989 and 2020 were extracted from the Trialtrove database.

RESULTS

Key aspects that may improve recruitment rates were identified. An increasingly patient-centric approach should be taken to study design including improvements to the readability of key trial documentation and inclusion of patient representatives in trial planning. Placebo is unappealing to patients; approaches including platform trials should be explored to minimise placebo exposure. Non-invasive imaging, biomarkers and novel digital endpoints should continue to be examined to reduce the burden on patients. Reducing the administrative burden associated with trials via the use of electronic signatures, for example, may benefit study sites and investigators. Changes implemented to IBD trials during the COVID-19 pandemic provided examples of how trial conduct can be rapidly and constructively adapted.

CONCLUSIONS

To improve recruitment in Crohn's disease and ulcerative colitis trials, the IBD community should address a broad range of issues related to clinical trial conduct.

ACE: the Advanced Cohort Engine for searching longitudinal patient records

OBJECTIVE

To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.

MATERIALS AND METHODS

The Advanced Cohort Engine (ACE) uses a temporal query language and in-memory datastore of patient objects to provide a fast, scalable, and expressive time-aware search. ACE accepts data in the Observational Medicine Outcomes Partnership Common Data Model, and is configurable to balance performance with compute cost. ACE's temporal query language supports automatic query expansion using clinical knowledge graphs. The ACE API can be used with R, Python, Java, HTTP, and a Web UI.

RESULTS

ACE offers an expressive query language for complex temporal search across many clinical data types with multiple output options. ACE enables electronic phenotyping and cohort-building with subsecond response times in searching the data of millions of patients for a variety of use cases.

DISCUSSION

ACE enables fast, time-aware search using a patient object-centric datastore, thereby overcoming many technical and design shortcomings of relational algebra-based querying. Integrating electronic phenotype development with cohort-building enables a variety of high-value uses for a learning health system. Tradeoffs include the need to learn a new query language and the technical setup burden.

CONCLUSION

ACE is a tool that combines a unique query language for time-aware search of longitudinal patient records with a patient object datastore for rapid electronic phenotyping, cohort extraction, and exploratory data analyses.

Practices and Attitudes of Swiss Stakeholders Regarding Investigator-Initiated Clinical Trial Funding Acquisition and Cost Management

IMPORTANCE

Randomized clinical trials (RCTs) are an essential method of evaluating health care interventions and a cornerstone for evidence-based health care. However, RCTs have become increasingly complex and costly, which is particularly challenging for independent investigator-initiated clinical trials (IICTs). IICTs have an essential role in clinical research, and it is important that efforts are made to ensure IICTs are adequately funded and are conducted cost-effectively.

OBJECTIVE

To examine the practices and attitudes of Swiss stakeholders regarding IICT funding acquisition and cost management.

DESIGN, SETTING, AND PARTICIPANTS

For this qualitative study, interviews were conducted in Switzerland between February and August 2020. The purposive sample comprised 48 stakeholders from 4 different groups: primary investigators (n = 27), funders and sponsors (n = 9), clinical trial support organizations (n = 6), and ethics committee members (n = 6).

MAIN OUTCOMES AND MEASURES

Practices and attitudes of stakeholders regarding IICT funding acquisition and cost management were assessed using individual semistructured qualitative interviews. Interviews were analyzed using conventional content analysis.

RESULTS

After interviews with 48 IICT stakeholders (75% male presenting), these participants identified a systemic problem of IICTs being underfunded, which can lead to compromises being made regarding the quality and conduct of IICTs. Participants identified 2 overarching and interconnected groups of reasons why IICTs in Switzerland are regularly underfunded. First, it was reported that IICT budget estimations are often inaccurate because of poor planning and preparation, unforeseeable events, investigators intentionally underestimating budgets, and limited budget assessment and oversight. Second, with the exception of a specific IICT funding program by the Swiss National Science Foundation, it was reported that limited funding sources and unrealistic expectation of funders led to underlying challenges in getting IICTs fully funded. A number of measures that could help reduce the underfunding of IICTs were identified, including improving the support of investigators and IICTs, strengthening networking and guidance, harmonizing and simplifying bureaucracy, and increasing public funding of IICTs.

CONCLUSIONS AND RELEVANCE

This study highlights the inadequate expertise of Swiss stakeholders to correctly, systematically, and reproducibly calculate RCT budgets and the need for transparency on trial costs as well as training in budgeting practices. Limited financial resources for academic clinical research and issues regarding the professional planning and conduct of IICTs are persistent issues that many other countries also face.

Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project

BACKGROUND

Data collection consumes a large proportion of clinical trial resources. Each data item requires time and effort for collection, processing and quality control procedures. In general, more data equals a heavier burden for trial staff and participants. It is also likely to increase costs. Knowing the types of data being collected, and in what proportion, will be helpful to ensure that limited trial resources and participant goodwill are used wisely.

AIM

The aim of this study is to categorise the types of data collected across a broad range of trials and assess what proportion of collected data each category represents.

METHODS

We developed a standard operating procedure to categorise data into primary outcome, secondary outcome and 15 other categories. We categorised all variables collected on trial data collection forms from 18, mainly publicly funded, randomised superiority trials, including trials of an investigational medicinal product and complex interventions. Categorisation was done independently in pairs: one person having in-depth knowledge of the trial, the other independent of the trial. Disagreement was resolved through reference to the trial protocol and discussion, with the project team being consulted if necessary.

KEY RESULTS

Primary outcome data accounted for 5.0% (median)/11.2% (mean) of all data items collected. Secondary outcomes accounted for 39.9% (median)/42.5% (mean) of all data items. Non-outcome data such as participant identifiers and demographic data represented 32.4% (median)/36.5% (mean) of all data items collected.

CONCLUSION

A small proportion of the data collected in our sample of 18 trials was related to the primary outcome. Secondary outcomes accounted for eight times the volume of data as the primary outcome. A substantial amount of data collection is not related to trial outcomes. Trialists should work to make sure that the data they collect are only those essential to support the health and treatment decisions of those whom the trial is designed to inform.

Improving Study Conduct and Data Quality in Clinical Trials of Chronic Pain Treatments: IMMPACT Recommendations

The estimated probability of progressing from phase 3 analgesic clinical trials to regulatory approval is approximately 57%, suggesting that a considerable number of treatments with phase 2 trial results deemed sufficiently successful to progress to phase 3 do not yield positive phase 3 results. Deficiencies in the quality of clinical trial conduct could account for some of this failure. An Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials meeting was convened to identify potential areas for improvement in trial conduct in order to improve assay sensitivity (ie, ability of trials to detect a true treatment effect). We present recommendations based on presentations and discussions at the meeting, literature reviews, and iterative revisions of this article. The recommendations relate to the following areas: 1) study design (ie, to promote feasibility), 2) site selection and staff training, 3) participant selection and training, 4) treatment adherence, 5) data collection, and 6) data and study monitoring. Implementation of these recommendations may improve the quality of clinical trial data and thus the validity and assay sensitivity of clinical trials. Future research regarding the effects of these strategies will help identify the most efficient use of resources for conducting high quality clinical trials. PERSPECTIVE: Every effort should be made to optimize the quality of clinical trial data. This manuscript discusses considerations to improve conduct of pain clinical trials based on research in multiple medical fields and the expert consensus of pain researchers and stakeholders from academia, regulatory agencies, and industry.

Keeping track of all ongoing colorectal cancer trials using a mobile application: Usability and satisfaction results of the Dutch Colorectal Cancer Group Trials application

BACKGROUND AND AIM

Both the number and complexity of medical trials are increasing vastly. To facilitate easy access to concise trial information, a freely available mobile application including all ongoing clinical trials of the Dutch Colorectal Cancer Group (DCCG) was developed. The aim of this study was to investigate the use and user satisfaction over the first 2 years.

METHODS

The application was launched in January 2015 on iOS and Android platforms. Google Analytics was used to monitor anonymous user data up to February 2017. In addition, an online survey regarding the use and satisfaction among health-care professionals and research affiliates active in the field of colorectal cancer in the Netherlands was conducted.

RESULTS

A total of 6173 unique users were identified, of which 1822 (30%) were from the Netherlands, representing a total of 16,065 and 10,987 (68%) sessions, respectively. The median session duration per day was 01:47 min (IQR 0:51-03:03). The mobile application was mostly used on Monday, Tuesday, and Thursday, and the number of sessions was highest during the following time frames: 12-13 pm (9%), 17-18 pm (9%), and 13-14 pm (8%). Of 121 survey responses, most were medical doctors (47%), nurses (25%), or researchers (9%), working either in a teaching (40%), academic hospital (32%), or general hospital (19%). 83% of all respondents rated the application 4 or higher for satisfaction on a 5-point scale. Highest reported reasons of the use were urgent trial inquiry (57%) and usage during multi-disciplinary meetings (49%).

CONCLUSION

The DCCG Trials application is frequently used, and the majority of users is highly satisfied.

RELEVANCE FOR PATIENTS

Clustering trial information into one platform, such as DCCG trials app, has shown to be useful for medical professionals treating patients with colorectal carcinoma in the Netherlands.

Increasing Efficiency of Recruitment in Early Parkinson's Disease Trials: A Case Study Examination of the STEADY-PD III Trial

Background:

Challenges in clinical trial recruitment threaten the successful development of improved therapies. This is particularly true in Parkinson’s disease (PD) studies of disease modification where the population of interest is difficult to find and study design is more complex.

Objective:

This paper seeks to understand how STEADY PD III, a National Institute of Neurological Disorders and Stroke (NINDS) funded phase 3 trial evaluating the efficacy of isradipine as a disease modifying agent for PD, was able to recruit their full target population 6 months ahead of schedule.

Methods:

STEADY PD III aimed to enroll 336 individuals with early stage idiopathic PD within 18 months using 57 sites across the United States and Canada. The study included a 10% NIH minority recruitment goal. Eligible participants agreed to be followed for up to 36 months, complete 12 in-person visits and 4 telephone visits. A Recruitment Committee of key stakeholders was critical in the development of a comprehensive recruitment strategy involving: multi-modal outreach, protocol modifications and comprehensive site selection and activation. Efforts to increase site-specific minority recruitment strategies were encouraged through additional funding.

Results:

A total of 336 individuals, including 34 minorities, were enrolled within 12 months – 6 months ahead of the projected timeline. Quantitative analysis of recruitment activity questionnaires found that of the sites that completed them (n = 54), (20.4%) met goals, (24.1%) exceeded goals, and (55.6%) fell below projected goals. Referral sources completed at time of screening indicate top four study referral sources as: site personnel (53.8%); neurologists (24%); Fox Trial Finder (10.2%); and communications from The Michael J. Fox Foundation (3.9%).

Conclusions:

STEADY PD III serves as an important example of methods that can be used to increase clinical trial recruitment. This research highlights a continued need to improve site infrastructure and dedicate more resources to increased participation of minorities in clinical research.

Use of Proxy Variables to Determine the Impact of Protocol Complexity on Clinical Research Site Productivity

BACKGROUND

Research coordinators (or teams) are usually assigned to multiple studies of varying complexity at any one time, each with different and ever-changing workloads. As a result, determining the impact of protocol complexity on productivity is not easily accomplished. Standard methods of effort tracking typically require oversight or create additional workload to the site staff under study; they are time-consuming, expensive, intrusive, and usually incomplete.

METHODS

This article describes a novel method for determining the impact of protocol complexity on clinical research coordinator (CRC) or team productivity by using proxy variables in place of effort tracking. A protocol assessment tool that quantitates complexity is used to determine cumulative workload.

RESULTS

Productivity graphs are generated for each CRC per month and can be followed over time to assess trends or for comparative analysis.

CONCLUSION

The data provide managers with unique insights into the functional capacity of study coordinators and support staff. The goal is to optimize efficiency by applying a systematic decision process from performance and productivity trends. In addition to exploring the theory behind the method, this article begins a discussion on the use of this information in clinical research site management.

Baseline Assessment of the Evolving 2017 eClinical Landscape

The volume and diversity of data collected to support each clinical study has increased dramatically in response to the rising scope and complexity of global drug development programs. The Tufts Center for the Study of Drug Development conducted an online survey of 257 unique global companies-77% drug development sponsors and 23% contract service providers-to assess clinical data management practices and experiences. Study results indicate that companies are using an average of 6 different applications to support each clinical study and that companies are collecting a range of data types including that from case report forms, lab procedures, pharmacokinetics, biomarker, outcomes assessment, mobile health, and social media. Companies report that the primary electronic data capture (EDC) is capturing traditional data types but not many of the newer ones. Respondents report spending an average of 68.3 days to build and release a study database, 8.1 days between the patient visit and when that patient's data are entered into the EDC system, and 36.3 days on average to lock the database following the last patient last visit. Average cycle time durations are longer and more variable than those observed ten years ago. Subgroup differences (eg, by company size and company type) and factors contributing to data management cycle time and experience are discussed.

Systematic review on costs and resource use of randomized clinical trials shows a lack of transparent and comprehensive data

OBJECTIVES

Randomized clinical trials (RCTs) are costly. We aimed to provide a systematic overview of the available evidence on resource use and costs for RCTs to support budget planning.

STUDY DESIGN AND SETTING

We systematically searched MEDLINE, EMBASE, and HealthSTAR from inception until November 30, 2016 without language restrictions. We included any publication reporting empirical data on resource use and costs of RCTs and categorized them depending on whether they reported (i) resource and costs of all aspects at all study stages of an RCT (including conception, planning, preparation, conduct, and all tasks after the last patient has completed the RCT); (ii) on several aspects, (iii) on a single aspect (e.g., recruitment); or (iv) on overall costs for RCTs. Median costs of different recruitment strategies were calculated. Other results (e.g., overall costs) were listed descriptively. All cost data were converted into USD 2017.

RESULTS

A total of 56 articles that reported on cost or resource use of RCTs were included. None of the articles provided empirical resource use and cost data for all aspects of an entire RCT. Eight articles presented resource use and cost data on several aspects (e.g., aggregated cost data of different drug development phases, site-specific costs, selected cost components). Thirty-five articles assessed costs of one specific aspect of an RCT (i.e., 30 on recruitment; five others). The median costs per recruited patient were USD 409 (range: USD 41-6,990). Overall costs of an RCT, as provided in 16 articles, ranged from USD 43-103,254 per patient, and USD 0.2-611.5 Mio per RCT but the methodology of gathering these overall estimates remained unclear in 12 out of 16 articles (75%).

CONCLUSION

The usefulness of the available empirical evidence on resource use and costs of RCTs is limited. Transparent and comprehensive resource use and cost data are urgently needed to support budget planning for RCTs and help improve sustainability.

Streamlining Safety Data Collection in Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia Trials: Recommendations of the Clinical Trials Transformation Initiative Antibacterial Drug Development Project Team

BACKGROUND

Resistant bacteria are one of the leading causes of hospital-acquired/ventilator-associated bacterial pneumonia (HABP/VABP). HABP/VABP trials are complex and difficult to conduct due to the large number of medical procedures, adverse events, and concomitant medications involved. Differences in the legislative frameworks between different regions of the world may also lead to excessive data collection. The Clinical Trials Transformation Initiative (CTTI) seeks to advance antibacterial drug development (ABDD) by streamlining clinical trials to improve efficiency and feasibility while maintaining ethical rigor, patient safety, information value, and scientific validity.

METHODS

In 2013, CTTI engaged a multidisciplinary group of experts to discuss challenges impeding the conduct of HABP/VABP trials. Separate workstreams identified challenges associated with current data collection processes. Experts defined "data collection" as the act of capturing and reporting certain data on the case report form as opposed to recording of data as part of routine clinical care. The ABDD Project Team developed strategies for streamlining safety data collection in HABP/VABP trials using a Quality by Design approach.

RESULTS

Current safety data collection processes in HABP/VABP trials often include extraneous information. More targeted strategies for safety data collection in HABP/VABP trials will rely on optimal protocol design and prespecification of which safety data are essential to satisfy regulatory reporting requirements.

CONCLUSIONS

A consensus and a cultural change in clinical trial design and conduct, which involve recognition of the need for more efficient data collection, are urgently needed to advance ABDD and to improve HABP/VABP trials in particular.

9-Valent human papillomavirus vaccine: a review of the clinical development program

INTRODUCTION

The 9-valent human papillomavirus (9vHPV) vaccine covers the same HPV types (6/11/16/18) as the quadrivalent HPV (qHPV) vaccine and 5 additional cancer-causing types (31/33/45/52/58). Epidemiological studies indicate that the 9vHPV vaccine could prevent approximately 90% of cervical cancers, 70-85% of high-grade cervical dysplasia (precancers), 85-95% of HPV-related vulvar, vaginal, and anal cancers, and 90% of genital warts. Areas covered: Study design features and key findings from the 9vHPV vaccine clinical development program are reviewed. In particular, 9vHPV vaccine efficacy was established in a Phase III study in young women age 16-26 years. Efficacy results in young women were extrapolated to pre- and young adolescent girls and boys and young men by immunological bridging (i.e., demonstration of non-inferior immunogenicity in these groups versus young women). Expert commentary: The development of the 9vHPV vaccine is the outcome of 20 years of continuous clinical research. Broad vaccination programs could help substantially decrease the incidence of HPV-related disease.

A comparative effectiveness study of eSource used for data capture for a clinical research registry

Objective

This pilot study compared eSource-enabled versus traditional manual data transcription (non-eSource methods) for the collection of clinical registry information. The primary study objective was to compare the time spent completing registry forms using eSource versus non-eSource methods The secondary objectives were to compare data quality associated with these two data capture methods and the flexibility of the workflows. This study directly addressed fundamental questions relating to eSource adoption: what time-savings can be realized, and to what extent does eSource improve data quality.

Materials and methods

The study used time and motion methods to compare eSource versus non-eSource data capture workflows for a single center OB/GYN registry. Direct observation by industrial engineers using specialized computer software captured keystrokes, mouse clicks and video recordings of the study team in their normal work environment completing real-time data collection.

Results

The overall average data capture time was reduced with eSource versus non-eSource methods (difference, 151 s per case; eSource, 1603 s; non-eSource, 1754 s; p = 0.051). The average data capture time for the demographic data was reduced (difference, 79 s per case; eSource, 133 s; non-eSource, 213 s; p < 0.001). This represents a 37% time reduction (95% confidence interval 27% to 47%). eSourced data field transcription errors were also reduced (eSource, 0%; non-eSource, 9%).

Conclusion

The use of eSource versus traditional data transcription was associated with a significant reduction in data entry time and data quality errors. Further studies in other settings are needed to validate these results.

Efficient design of clinical trials and epidemiological research: is it possible?

Randomized clinical trials and large-scale, cohort studies continue to have a critical role in generating evidence in cardiovascular medicine; however, the increasing concern is that ballooning costs threaten the clinical trial enterprise. In this Perspectives article, we discuss the changing landscape of clinical research, and clinical trials in particular, focusing on reasons for the increasing costs and inefficiencies. These reasons include excessively complex design, overly restrictive inclusion and exclusion criteria, burdensome regulations, excessive source-data verification, and concerns about the effect of clinical research conduct on workflow. Thought leaders have called on the clinical research community to consider alternative, transformative business models, including those models that focus on simplicity and leveraging of digital resources. We present some examples of innovative approaches by which some investigators have successfully conducted large-scale, clinical trials at relatively low cost. These examples include randomized registry trials, cluster-randomized trials, adaptive trials, and trials that are fully embedded within digital clinical care or administrative platforms.

Increasing protocol suitability for clinical trials in sub-Saharan Africa: a mixed methods study

Background

The trial protocol is the most important document for clinical trials and describes not only the design and methodology of a study, but also all practical aspects. The suitability of the protocol has a direct impact on the execution and results of the trial. However, suitability is rarely addressed in trial practice and research. The aim of our study was to investigate protocol suitability and to identify suitability-enhancing measures for trials in sub-Saharan Africa.

Methods

We used an exploratory mixed methods design. First, we interviewed 36 trial staff at different organisational levels in Ghana, Burkina Faso and Senegal. Second, we conducted an online survey among trial staff in sub-Saharan Africa to investigate trial protocol suitability based on the main themes distilled from the interviews.

Results

Protocol suitability surfaced as a prominent topic in interviews with trial staff, critiqued for its lack of clarity, implementability and adaptation to trial participants as well as to the workforce and infrastructure available. Both qualitative and quantitative investigations identified local site staff involvement in protocol development as the most helpful mean of increasing protocol suitability. Careful assessment of the local context, capacity and cultures, and ensuring that staff understand the protocol were also cited as helpful measures.

Conclusions

Our data suggests that protocol suitability can be increased by discussing and reviewing the protocol with trial staff in advance. Involving operationally experienced staff would be most useful. For multicentre trials, we suggest that at least one trial staff member from each of the sites with the highest expected recruitment rates be involved in developing the protocol. Carefully assessing the context prior to study start is indispensable to ensuring protocol suitability and should particularly focus on the workforce and infrastructure available, as well as the needs and availability of trial participants. To allow for protocol suitability enhancing measures, planners must allocate enough time for trial preparation and solicit feedback and information on context at an early stage. Such prospective planning would increase implementability, efficiency and quality of trials in the long run.

Electronic supplementary material

The online version of this article (doi:10.1186/s41256-017-0031-1) contains supplementary material, which is available to authorized users.

"You can save time if…"-A qualitative study on internal factors slowing down clinical trials in Sub-Saharan Africa

BACKGROUND

The costs, complexity, legal requirements and number of amendments associated with clinical trials are rising constantly, which negatively affects the efficient conduct of trials. In Sub-Saharan Africa, this situation is exacerbated by capacity and funding limitations, which further increase the workload of clinical trialists. At the same time, trials are critically important for improving public health in these settings. The aim of this study was to identify the internal factors that slow down clinical trials in Sub-Saharan Africa. Here, factors are limited to those that exclusively relate to clinical trial teams and sponsors. These factors may be influenced independently of external conditions and may significantly increase trial efficiency if addressed by the respective teams.

METHODS

We conducted sixty key informant interviews with clinical trial staff working in different positions in two clinical research centres in Kenya, Ghana, Burkina Faso and Senegal. The study covered English- and French-speaking, and Eastern and Western parts of Sub-Saharan Africa. We performed thematic analysis of the interview transcripts.

RESULTS

We found various internal factors associated with slowing down clinical trials; these were summarised into two broad themes, "planning" and "site organisation". These themes were consistently mentioned across positions and countries. "Planning" factors related to budget feasibility, clear project ideas, realistic deadlines, understanding of trial processes, adaptation to the local context and involvement of site staff in planning. "Site organisation" factors covered staff turnover, employment conditions, career paths, workload, delegation and management.

CONCLUSIONS

We found that internal factors slowing down clinical trials are of high importance to trial staff. Our data suggest that adequate and coherent planning, careful assessment of the setting, clear task allocation and management capacity strengthening may help to overcome the identified internal factors and allow clinical trials to proceed more efficiently.

Assessment and classification of protocol deviations

INTRODUCTION

Deviations from the approved trial protocol are common during clinical trials. They have been conventionally classified as deviations or violations, depending on their impact on the trial.

METHODS

A new method has been proposed by which deviations are classified in five grades from 1 to 5. A deviation of Grade 1 has no impact on the subjects' well-being or on the quality of data. At the maximum, a deviation Grade 5 leads to the death of the subject. This method of classification was applied to deviations noted in the center over the last 3 years.

RESULTS

It was observed that most deviations were of Grades 1 and 2, with fewer falling in Grades 3 and 4. There were no deviations that led to the death of the subject (Grade 5).

DISCUSSION

This method of classification would help trial managers decide on the action to be taken on the occurrence of deviations, which would be based on their impact.

User Satisfaction Evaluation of the EHR4CR Query Builder: A Multisite Patient Count Cohort System

The Electronic Health Records for Clinical Research (EHR4CR) project aims to develop services and technology for the leverage reuse of Electronic Health Records with the purpose of improving the efficiency of clinical research processes. A pilot program was implemented to generate evidence of the value of using the EHR4CR platform. The user acceptance of the platform is a key success factor in driving the adoption of the EHR4CR platform; thus, it was decided to evaluate the user satisfaction. In this paper, we present the results of a user satisfaction evaluation for the EHR4CR multisite patient count cohort system. This study examined the ability of testers (n = 22 and n = 16 from 5 countries) to perform three main tasks (around 20 minutes per task), after a 30-minute period of self-training. The System Usability Scale score obtained was 55.83 (SD: 15.37), indicating a moderate user satisfaction. The responses to an additional satisfaction questionnaire were positive about the design of the interface and the required procedure to design a query. Nevertheless, the most complex of the three tasks proposed in this test was rated as difficult, indicating a need to improve the system regarding complicated queries.

Efficiency and effectiveness evaluation of an automated multi-country patient count cohort system

Background

With the increase of clinical trial costs during the last decades, the design of feasibility studies has become an essential process to reduce avoidable and costly protocol amendments. This design includes timelines, targeted sites and budget, together with a list of eligibility criteria that potential participants need to match.

The present work was designed to assess the value of obtaining potential study participant counts using an automated patient count cohort system for large multi-country and multi-site trials: the Electronic Health Records for Clinical Research (EHR4CR) system.

Methods

The evaluation focuses on the accuracy of the patient counts and the time invested to obtain these using the EHR4CR platform compared to the current questionnaire based process. This evaluation will assess the patient counts from ten clinical trials at two different sites. In order to assess the accuracy of the results, the numbers obtained following the two processes need to be compared to a baseline number, the “alloyed” gold standard, which was produced by a manual check of patient records.

Results

The patient counts obtained using the EHR4CR system were in three evaluated trials more accurate than the ones obtained following the current process whereas in six other trials the current process counts were more accurate. In two of the trials both of the processes had counts within the gold standard’s confidence interval.

In terms of efficiency the EHR4CR protocol feasibility system proved to save approximately seven calendar days in the process of obtaining patient counts compared to the current manual process.

Conclusions

At the current stage, electronic health record data sources need to be enhanced with better structured data so that these can be re-used for research purposes. With this kind of data, systems such as the EHR4CR are able to provide accurate objective patient counts in a more efficient way than the current methods.

Additional research using both structured and unstructured data search technology is needed to assess the value of unstructured data and to compare the amount of efforts needed for data preparation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-015-0035-9) contains supplementary material, which is available to authorized users.

The clinical trial model is up for review: time, expense, and quality of results are at issue, as is the relationship to drug pricing

Costly medications have raised questions about the role that long, complex clinical trials play in drug pricing. Approval of a drug takes, on average, 14 years, costs more than $2 billion, and has a high risk of failure. Participants are seeking alternatives.