Informatics tools to improve clinical research study implementation

"Reality is frequently inaccurate" A case study examining the whens and whys of post-live database changes in a UK clinical trials unit *Douglas Adams

INTRODUCTION

Accurately estimating the costs of clinical trials is challenging. There is currently no reference class data to allow researchers to understand the potential costs associated with database change management in clinical trials.

METHODS

We used a case-based approach, summarising post-live changes in eleven clinical trial databases managed by Sheffield Clinical Trials Research Unit. We reviewed the database specifications for each trial and summarised the number of changes, change type, change category, and timing of changes. We pooled our experiences and made observations in relation to key themes.

RESULTS

Median total number of changes across the eleven trials was 71 (range 40-155) and median number of changes per study week was 0.48 (range 0.32-1.34). The most common change type was modification (median 39, range 20-90), followed by additions (median 32, range 18-55), then deletions (median 7, range 1-12). In our sample, changes were more common in the first half of the trial's lifespan, regardless of its overall duration. Trials which saw continuous changes seemed more likely to be external pilots or trials in areas where the trial team was either less experienced overall or within the particular therapeutic area.

CONCLUSIONS

Researchers should plan trials with the expectation that clinical trial databases will require changes within the life of the trial, particularly in the early stages or with a less experienced trial team. More research is required to understand potential differences between clinical trial units and database types.

Protocolization of multicenter clinical studies in the digital era. Is useful data centralization by a data-manager?

INTRODUCTION

In multicenter studies, the protocolization of data is a critical phase that can generate biases.The objective is to analyze the concordance and reliability of the data obtained in a clinical multicenter study between the protocolization in the center of origin and the centralized protocolization of the data by a data -manager.

METHODS

National multicenter clinical study about an infrequent carcinoma. A double protocolization of the data is carried out: (a) center of origin; and (b) centralized by a data manager: The concordance between the data is analyzed for the global data and for the two groups of the project: (a) study group (Familiar carcinoma, 30 researchers protocolize); (b) control group (Sporadic carcinoma, 4 people protocolize). Interobserver variability is evaluated using Cohen's kappa coefficient.

RESULTS

The study includes a total of 689 patients with carcinoma, 252 in the study group and 437 in the control group. Regarding the concordance analysis of the tumor stage, 2.5% of disagreements were observed and the concordance between people who protocolize was near perfect (Kappa = 0.931). Regarding the evaluation of the recurrence risk, disagreements occurred in 7% of the cases and the concordance was near perfect (Kappa = 0.819). Regarding the sonography evaluation (TIRADS), the disagreements were 6.9% and the concordance was near perfect (Kappa = 0.922). Also, 4.6% of transcription errors were detected.

CONCLUSIONS

In multicenter clinical studies, the centralized data protocolization o by a data-manager seems to present similar results to the direct protocolization in the database in the center of origin.

Electronic versus paper-based data collection for conducting health-care research: A cost-comparison analysis

Background

Containing expenditure and efficient resource use is essential to limit the increasing costs of health research. Electronic data collection (EDC) is thought to reduce the costs compared to paper-based data collection (PDC).

Objectives

As economic evidence in this area is scanty, especially in low- and middle-income countries, the objectives of the study are to perform an economic evaluation and compare the cost between EDC and PDC.

Methods

A cost-comparison study was conducted to compare between EDC and PDC from the institutional perspective for the year 2018, based on a community-based survey. Step-down cost accounting was adopted with a bottom-up approach for cost estimation. Total and unit costs were estimated with the base case comparison between EDC and PDC while using SPSS software (e-SPSS and p-SPSS, respectively). We conducted scenario analyses based on the usage of different software, R and STATA for both EDC and PDC (e-R, p-R, e-STATA, and p-STATA, respectively). One-way and probabilistic sensitivity analysis (PSA) was performed to examine the robustness of the observed results.

Results

In the base-case analysis, total costs of EDC and PDC were ₹72,617 ($1060.9) and 87,717 ($1281.5), respectively, with estimated cost reduction of ₹15,100 ($220.6). In other scenarios, the estimated cost reduction for e-R, e-STATA, p-R, p-STATA was ₹-274 ($4.0), 98 ($1.4), 14826 ($216.6), and 15,002 ($219.2), respectively, when compared to EDC-SPSS. On one-way and PSA, the results of the cost-comparison analysis were robust.

Conclusion

EDC minimizes institutional cost for conducting health research. This finding will help researchers in efficiently planning for the budget for their research.

FIMED: Flexible management of biomedical data

BACKGROUND AND OBJECTIVES

In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are currently not able to cover the needs of adaptation to the continuous changes in the practice of the trials due to the heterogeneous and dynamic nature of the clinical research data. These systems are usually proprietary solutions provided by vendors for specific tasks. In this work, we propose FIMED, a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials.

METHODS

This tool allows a dynamic and incremental design of patients' profiles in the context of clinical trials, providing a flexible user interface that hides the complexity of using databases. Clinical researchers will be able to define personalized data schemas according to their needs and clinical study specifications. Thus, FIMED allows the incorporation of separate clinical data analysis from multiple trials.

RESULTS

The efficiency of the software has been demonstrated by a real-world use case for a clinical assay in Melanoma disease, which has been indeed anonymized to provide a user demonstration. FIMED currently provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data: heatmap visualization, clusterheatmap visualization, as well as gene regulatory network inference and visualization. An instance of this tool is freely available on the web at https://khaos.uma.es/fimed. It can be accessed with a demo user account, "researcher", using the password "demo".

CONCLUSION

This paper shows FIMED as a flexible and user-friendly way of managing multidimensional clinical research data. Hence, without loss of generality, FIMED is flexible enough to be used in the context of any other disease where clinical data and assays are involved.

Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research: Implementation, Evaluation, and Validation Study

BACKGROUND

Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also at risk of being mishandled by investigators.

OBJECTIVE

The urgent need to assure the highest data quality possible has led to the implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field. The objective of this study was to describe a machine learning-based algorithm to detect anomalous patterns in data created as a consequence of carelessness, systematic error, or intentionally by entering fabricated values.

METHODS

A particular electronic data capture (EDC) system, which is used for data management in clinical registries, is presented including its architecture and data structure. This EDC system features an algorithm based on machine learning designed to detect anomalous patterns in quantitative data. The detection algorithm combines clustering with a series of 7 distance metrics that serve to determine the strength of an anomaly. For the detection process, the thresholds and combinations of the metrics were used and the detection performance was evaluated and validated in the experiments involving simulated anomalous data and real-world data.

RESULTS

Five different clinical registries related to neuroscience were presented-all of them running in the given EDC system. Two of the registries were selected for the evaluation experiments and served also to validate the detection performance on an independent data set. The best performing combination of the distance metrics was that of Canberra, Manhattan, and Mahalanobis, whereas Cosine and Chebyshev metrics had been excluded from further analysis due to the lowest performance when used as single distance metric-based classifiers.

CONCLUSIONS

The experimental results demonstrate that the algorithm is universal in nature, and as such may be implemented in other EDC systems, and is capable of anomalous data detection with a sensitivity exceeding 85%.

EsPRit: ethics committee proposals for Long Term Medical Data Registries in rapidly evolving research fields - a future-proof best practice approach

Background

Long-term data collection is a challenging task in the domain of medical research. Many effects in medicine require long periods of time to become traceable e.g. the development of secondary malignancies based on a given radiotherapeutic treatment of the primary disease. Nevertheless, long-term studies often suffer from an initial lack of available information, thus disallowing a standardized approach for their approval by the ethics committee. This is due to several factors, such as the lack of existing case report forms or an explorative research approach in which data elements may change over time.

In connection with current medical research and the ongoing digitalization in medicine, Long Term Medical Data Registries (MDR-LT) have become an important means of collecting and analyzing study data. As with any clinical study, ethical aspects must be taken into account when setting up such registries.

This work addresses the problem of creating a valid, high-quality ethics committee proposal for medical registries by suggesting groups of tasks (building blocks), information sources and appropriate methods for collecting and analyzing the information, as well as a process model to compile an ethics committee proposal (EsPRit).

Methods

To derive the building blocks and associated methods software and requirements engineering approaches were utilized. Furthermore, a process-oriented approach was chosen, as information required in the creating process of ethics committee proposals remain unknown in the beginning of planning an MDR-LT. Here, we derived the needed steps from medical product certification. This was done as the medical product certification itself also communicates a process-oriented approach rather than merely focusing on content. A proposal was created for validation and inspection of applicability by using the proposed building blocks. The proposed best practice was tested and refined within SEMPER (Secondary Malignoma - Prospective Evaluation of the Radiotherapeutics dose distribution as the cause for induction) as a case study.

Results

The proposed building blocks cover the topics of “Context Analysis”, “Requirements Analysis”, “Requirements Validation”, “Electronic Case Report (eCRF) Design” and “Overall Concept Creation”. Additional methods are attached with regards to each topic. The goals of each block can be met by applying those methods. The proposed methods are proven methods as applied in e.g. existing Medical Data Registry projects, as well as in software or requirements engineering.

Conclusion

Several building blocks and attached methods could be identified in the creation of a generic ethics committee proposal. Hence, an Ethics Committee can make informed decisions on the suggested study via said blocks, using the suggested methods such as “Defining Clinical Questions” within the Context Analysis. The study creators have to confirm that they adhere to the proposed procedure within the ethic proposal statement. Additional existing Medical Data Registry projects can be compared to EsPRit for conformity to the proposed procedure. This allows for the identification of gaps, which can lead to amendments requested by the ethics committee.

Data management in clinical research: Synthesizing stakeholder perspectives

OBJECTIVE

This study assesses data management needs in clinical research from the perspectives of researchers, software analysts and developers.

MATERIALS AND METHODS

This is a mixed-methods study that employs sublanguage analysis in an innovative manner to link the assessments. We performed content analysis using sublanguage theory on transcribed interviews conducted with researchers at four universities. A business analyst independently extracted potential software features from the transcriptions, which were translated into the sublanguage. This common sublanguage was then used to create survey questions for researchers, analysts and developers about the desirability and difficulty of features. Results were synthesized using the common sublanguage to compare stakeholder perceptions with the original content analysis.

RESULTS

Individual researchers exhibited significant diversity of perspectives that did not correlate by role or site. Researchers had mixed feelings about their technologies, and sought improvements in integration, interoperability and interaction as well as engaging with study participants. Researchers and analysts agreed that data integration has higher desirability and mobile technology has lower desirability but disagreed on the desirability of data validation rules. Developers agreed that data integration and validation are the most difficult to implement.

DISCUSSION

Researchers perceive tasks related to study execution, analysis and quality control as highly strategic, in contrast with tactical tasks related to data manipulation. Researchers have only partial technologic support for analysis and quality control, and poor support for study execution.

CONCLUSION

Software for data integration and validation appears critical to support clinical research, but may be expensive to implement. Features to support study workflow, collaboration and engagement have been underappreciated, but may prove to be easy successes. Software developers should consider the strategic goals of researchers with regard to the overall coordination of research projects and teams, workflow connecting data collection with analysis and processes for improving data quality.

Open-source electronic data capture system offered increased accuracy and cost-effectiveness compared with paper methods in Africa

Objectives

Existing electronic data capture options are often financially unfeasible in resource-poor settings or difficult to support technically in the field. To help facilitate large-scale multicenter studies in sub-Saharan Africa, the African Partnership for Chronic Disease Research (APCDR) has developed an open-source electronic questionnaire (EQ).

Study Design and Setting

To assess its relative validity, we compared the EQ against traditional pen-and-paper methods using 200 randomized interviews conducted in an ongoing type 2 diabetes case–control study in South Africa.

Results

During its 3-month validation, the EQ had a lower frequency of errors (EQ, 0.17 errors per 100 questions; paper, 0.73 errors per 100 questions; P-value ≤0.001), and a lower monetary cost per correctly entered question, compared with the pen-and-paper method. We found no marked difference in the average duration of the interview between methods (EQ, 5.4 minutes; paper, 5.6 minutes).

Conclusion

This validation study suggests that the EQ may offer increased accuracy, similar interview duration, and increased cost-effectiveness compared with paper-based data collection methods. The APCDR EQ software is freely available (https://github.com/apcdr/questionnaire).

The practice of 'doing' evaluation: lessons learned from nine complex intervention trials in action

Background

There is increasing recognition among trialists of the challenges in understanding how particular ‘real-life’ contexts influence the delivery and receipt of complex health interventions. Evaluations of interventions to change health worker and/or patient behaviours in health service settings exemplify these challenges. When interpreting evaluation data, deviation from intended intervention implementation is accounted for through process evaluations of fidelity, reach, and intensity. However, no such systematic approach has been proposed to account for the way evaluation activities may deviate in practice from assumptions made when data are interpreted.

Methods

A collective case study was conducted to explore experiences of undertaking evaluation activities in the real-life contexts of nine complex intervention trials seeking to improve appropriate diagnosis and treatment of malaria in varied health service settings. Multiple sources of data were used, including in-depth interviews with investigators, participant-observation of studies, and rounds of discussion and reflection.

Results and discussion

From our experiences of the realities of conducting these evaluations, we identified six key ‘lessons learned’ about ways to become aware of and manage aspects of the fabric of trials involving the interface of researchers, fieldworkers, participants and data collection tools that may affect the intended production of data and interpretation of findings. These lessons included: foster a shared understanding across the study team of how individual practices contribute to the study goals; promote and facilitate within-team communications for ongoing reflection on the progress of the evaluation; establish processes for ongoing collaboration and dialogue between sub-study teams; the importance of a field research coordinator bridging everyday project management with scientific oversight; collect and review reflective field notes on the progress of the evaluation to aid interpretation of outcomes; and these approaches should help the identification of and reflection on possible overlaps between the evaluation and intervention.

Conclusion

The lessons we have drawn point to the principle of reflexivity that, we argue, needs to become part of standard practice in the conduct of evaluations of complex interventions to promote more meaningful interpretations of the effects of an intervention and to better inform future implementation and decision-making.

Comparison of two data collection processes in clinical studies: electronic and paper case report forms

Background

Electronic Case Report Forms (eCRFs) are increasingly chosen by investigators and sponsors of clinical research instead of the traditional pen-and-paper data collection (pCRFs). Previous studies suggested that eCRFs avoided mistakes, shortened the duration of clinical studies and reduced data collection costs.

Methods

Our objectives were to describe and contrast both objective and subjective efficiency of pCRF and eCRF use in clinical studies. A total of 27 studies (11 eCRF, 16 pCRF) sponsored by the Paris hospital consortium, conducted and completed between 2001 and 2011 were included. Questionnaires were emailed to investigators of those studies, as well as clinical research associates and data managers working in Paris hospitals, soliciting their level of satisfaction and preferences for eCRFs and pCRFs. Mean costs and timeframes were compared using bootstrap methods, linear and logistic regression.

Results

The total cost per patient was 374€ ±351 with eCRFs vs. 1,135€ ±1,234 with pCRFs. Time between the opening of the first center and the database lock was 31.7 months Q1 = 24.6; Q3 = 42.8 using eCRFs, vs. 39.8 months Q1 = 31.7; Q3 = 52.2 with pCRFs (p = 0.11). Electronic CRFs were globally preferred by all (31/72 vs. 15/72 for paper) for easier monitoring and improved data quality.

Conclusions

This study found that eCRFs and pCRFs are used in studies with different patient numbers, center numbers and risk. The first ones are more advantageous in large, low–risk studies and gain support from a majority of stakeholders.

[Public clinical trials: which kind of monitoring should be used?]

OBJECTIVE

Sponsors must take responsibility for the quality of trials at the best possible cost. Our objective was to describe the most frequent quality failures, how they impact trial results, and identify the most efficient monitoring strategies using published articles and reports.

RESULTS

Errors affecting clinical trials include conception, procedures, data management, and data analysis. The consequences are usually an overestimation of the treatment effect. No study shows that monitoring reduces the risk of errors, and there is no comparison of monitoring methods. Many research organisations advocate for monitoring based on risk analysis and recommend an extensive use of centralised monitoring.

CONCLUSIONS

Trial quality depends on trial conception and design. Study conduct should guarantee a maximum level of quality level. This should be done using risk management and extensive centralised monitoring.

Use of an electronic medical record "dotphrase" data template for a prospective head injury study

Introduction:

The adoption of electronic medical records (EMRs) in emergency departments (EDs) has changed the way that healthcare information is collected, charted, and stored. A challenge for researchers is to determine how EMRs may be leveraged to facilitate study data collection efforts. Our objective is to describe the use of a unique data collection system leveraging EMR technology and to compare data entry error rates to traditional paper data collection.

Methods:

This was a retrospective review of data collection methods during a multicenter study of ED, anti-coagulated, head injury patients. On-shift physicians at 4 centers enrolled patients and prospectively completed data forms. These physicians had the option of completing a paper data form or an electronic “dotphrase” (DP) data form. A feature of our Epic®-based EMR is the ability to use DPs to assist in medical information entry. A DP is a preset template that may be inserted into the EMR when the physician types a period followed by a code phrase (in this case “.ichstudy”). Once the study DP was inserted at the bottom of the electronic ED note, it prompted enrolling physicians to answer study questions. Investigators then extracted data directly from the EMR.

Results:

From July 2009 through December 2010, we enrolled 883 patients. DP data forms were used in 288 (32.6%; 95% confidence interval [CI] 29.5, 35.7%) cases and paper data forms in 595 (67.4%; 95% CI 64.3, 70.5%). Sixty-six (43.7%; 95% CI 35.8, 51.6%) of 151 physicians enrolling patients used DP data entry at least once. Using multivariate analysis, we found no association between physician age, gender, or tenure and DP use. Data entry errors were more likely on paper forms (234/595, 39.3%; 95% CI 35.4, 43.3%) than DP forms (19/288, 6.6%; 95% CI 3.7, 9.5%), difference in error rates 32.7% (95% CI 27.9, 37.6%, P < 0.001).

Conclusion:

DP data collection is a feasible means of data collection. DP data forms maintain all study data within the secure EMR environment, obviating the need to maintain and collect paper data forms. This innovation was embraced by many of our emergency physicians and resulted in lower data entry error rates.

TRIAD: The Translational Research Informatics and Data Management Grid

OBJECTIVE

Multi-disciplinary and multi-site biomedical research programs frequently require infrastructures capable of enabling the collection, management, analysis, and dissemination of heterogeneous, multi-dimensional, and distributed data and knowledge collections spanning organizational boundaries. We report on the design and initial deployment of an extensible biomedical informatics platform that is intended to address such requirements.

METHODS

A common approach to distributed data, information, and knowledge management needs in the healthcare and life science settings is the deployment and use of a service-oriented architecture (SOA). Such SOA technologies provide for strongly-typed, semantically annotated, and stateful data and analytical services that can be combined into data and knowledge integration and analysis "pipelines." Using this overall design pattern, we have implemented and evaluated an extensible SOA platform for clinical and translational science applications known as the Translational Research Informatics and Data-management grid (TRIAD). TRIAD is a derivative and extension of the caGrid middleware and has an emphasis on supporting agile "working interoperability" between data, information, and knowledge resources.

RESULTS

Based upon initial verification and validation studies conducted in the context of a collection of driving clinical and translational research problems, we have been able to demonstrate that TRIAD achieves agile "working interoperability" between distributed data and knowledge sources.

CONCLUSION

Informed by our initial verification and validation studies, we believe TRIAD provides an example instance of a lightweight and readily adoptable approach to the use of SOA technologies in the clinical and translational research setting. Furthermore, our initial use cases illustrate the importance and efficacy of enabling "working interoperability" in heterogeneous biomedical environments.

A nutrient approach to prostate cancer prevention: The Selenium and Vitamin E Cancer Prevention Trial (SELECT)

The Selenium and Vitamin E Cancer Prevention Trial (SELECT) randomized 35,533 healthy men, >55 yr old (>50 yr if African American), with normal digital rectal exams and prostate specific antigens <4 ng/ml to 1) 200 μg/day l-selenomethionine, 2) 400 IU/day all-rac-alpha-tocopheryl acetate (vitamin E), 3) both supplements, or 4) placebo for 7 to 12 yr. The hypotheses underlying SELECT, that selenium and vitamin E individually and together decrease prostate cancer incidence, derived from epidemiologic and laboratory evidence and significant secondary endpoints in the Nutritional Prevention of Cancer (selenium) and Alpha-Tocopherol Beta-Carotene (vitamin E) trials. In SELECT, prostate cancer incidence did not differ among the 4 arms: hazard ratios [99% confidence intervals (CIs)] for prostate cancer were 1.13 (99% CI = 0.95-1.35, P = 0.06; n = 473) for vitamin E, 1.04 (99% CI = 0.87-1.24, P = 0.62; n = 432) for selenium, and 1.05 (99% CI = 0.88-1.25, P = 0.52; n = 437) for selenium + vitamin E vs. 1.00 (n = 416) for placebo. Statistically nonsignificant increased risks of prostate cancer with vitamin E alone [relative risk (RR) = 1.13, P = 0.06) and newly diagnosed Type 2 diabetes mellitus with selenium alone (RR = 1.07, P = 0.16) were observed. SELECT data show that neither selenium nor vitamin E, alone or together, in the doses and formulations used, prevented prostate cancer in this heterogeneous population of healthy men.

A patient-completed and optically read data acquisition system for clinical trials

Data collection and management within multicentre clinical trials can be challenging. We describe an adaptation of Teleform® technology to enable data recording by patients and their families on teleforms faxed and optically read directly into an electronic database, eliminating the need for case report forms. Preliminary results from a modest study sample size support the use of optically read forms for data collection by patients and their families, requiring only a pen, paper, and fax machine at participating sites.

Technical challenges in designing post-marketing eCRFs to address clinical safety and pharmacovigilance needs

To identify key challenges and propose technical considerations in designing electronic case report form (eCRF) for post-marketing studies, the author undertakes a comprehensive literature review of peer reviewed and grey literature to assess the key aspects, processes, standards, recommendations, and best practices in designing eCRFs based on industry experience in designing and supporting electronic data capture (EDC) studies. Literature search using strings on MEDLINE and PUBMED returned few papers directly related to CRF design. Health informatics and general practice journals were searched and results reviewed. Many conference, government commission, health professional and special interests group websites provide relevant information from practical experience - summarization of this information is presented. Further, we presented a list of concrete technical considerations in dealing with EDC technology/system limitations based on literature assessment and industry implementation experience. It is recognized that cross-functional teams be involved in eCRF design process and decision making. To summarize the keys in designing eCRFs to address post-market study safety and pharmacovigilance needs, the first is to identify required data elements from the study protocol supporting data analyses and reporting requirements. Secondly, accepted best practices, CDASH & CDISC guidelines, and company internal or therapeutic unit standard should be considered and applied. Coding (MedDRA & WHODD) mapping should be managed and implemented as well when possible. Finally, we need to be on top of the EDC technologies, challenge the technologies, drive EDC improvement via working with vendors, and utilize the technologies to drive clinical effectiveness.

Translational informatics: enabling high-throughput research paradigms

A common thread throughout the clinical and translational research domains is the need to collect, manage, integrate, analyze, and disseminate large-scale, heterogeneous biomedical data sets. However, well-established and broadly adopted theoretical and practical frameworks and models intended to address such needs are conspicuously absent in the published literature or other reputable knowledge sources. Instead, the development and execution of multidisciplinary, clinical, or translational studies are significantly limited by the propagation of "silos" of both data and expertise. Motivated by this fundamental challenge, we report upon the current state and evolution of biomedical informatics as it pertains to the conduct of high-throughput clinical and translational research and will present both a conceptual and practical framework for the design and execution of informatics-enabled studies. The objective of presenting such findings and constructs is to provide the clinical and translational research community with a common frame of reference for discussing and expanding upon such models and methodologies.

The University of Michigan Honest Broker: a Web-based service for clinical and translational research and practice

For the success of clinical and translational science, a seamless interoperation is required between clinical and research information technology. Addressing this need, the Michigan Clinical Research Collaboratory (MCRC) was created. The MCRC employed a standards-driven Web Services architecture to create the U-M Honest Broker, which enabled sharing of clinical and research data among medical disciplines and separate institutions. Design objectives were to facilitate sharing of data, maintain a master patient index (MPI), deidentification of data, and routing data to preauthorized destination systems for use in clinical care, research, or both. This article describes the architecture and design of the U-M HB system and the successful demonstration project. Seventy percent of eligible patients were recruited for a prospective study examining the correlation between interventional cardiac catheterizations and depression. The U-M Honest Broker delivered on the promise of using structured clinical knowledge shared among providers to help clinical and translational research.

Comparison of paper-based and electronic data collection process in clinical trials: costs simulation study

An alternative to clinical trial paper-based data collection (PDC) is internet based electronic data collection (EDC), where the investigators over the internet enter data directly in the electronic database by themselves. In our study we considered clinical trial as a business process. Our objective was to model PDC and EDC process and to estimate the difference of the costs of PDC and EDC process for a sample clinical trial based on these models. We used Extended Event-driven Process Chains (eEPC) modeling technique to model PDC and EDC process. In order to evaluate the costs of the processes we assigned costs functions to each process function which appears in the model. The parameters which appear in these functions include efforts, staff prices and data quality parameters. We estimated the values of all these parameters and performed costs calculations for a sample clinical trial. Through an analysis and modeling efforts we identified sub-processes which contain main differences affecting duration and costs of the PDC and EDC process: data gathering at the research center; monitoring; and data management. The most significant model difference between PDC and EDC process appeared in data management sub-process. For the sample clinical trial considered in our simulation study and our parameters estimations the EDC process decreased data collection costs for 55%. For different scenarios of parameters variations we show that the EDC process may bring from 49% to 62% of savings when compared to PDC process.

Assessing the impact of protocol design changes on clinical trial performance

Although it is widely acknowledged that protocol design plays a crucial role in the success of clinical research studies, how protocols have changed over time and the impact of these changes on clinical trial performance have never been quantified. To measure protocol design trends, the Tufts Center for the Study of Drug Development analyzed data on 10,038 unique phase 1-4 protocols conducted between 1999 and 2005. Tufts Center for the Study of Drug Development analyzed study conduct performance data on 57 individual phase 2 and 3 protocols administered at US-based investigative sites. The results of this study indicate that the number of unique procedures and the frequency of procedures per protocol have increased at the annual rate of 6.5% and 8.7%, respectively, during the time period measured. Investigative site work burden to administer each protocol increased at an even faster rate of 10.5% between 1999 and 2005. Additionally, during this time period, study conduct performance--that is, cycle time and patient recruitment and retention rates--worsened; and the number of protocol amendments, observed serious adverse events, and length of case report forms increased substantially. Implications of these results for simplifying protocol designs and minimizing negative effects on study conduct performance are discussed.

An automated standardized system for managing adverse events in clinical research networks

Multi-site clinical protocols and clinical research networks require tools to manage and monitor adverse events (AEs). To be successful, these tools must be designed to comply with applicable regulatory requirements, reflect current data standards, international directives and advances in pharmacovigilance, and be convenient and adaptable to multiple needs. We describe an Adverse Event Data Management System (AEDAMS) that is used across multiple study designs in the various clinical research networks and multi-site studies for which we provide data and technological support. Investigators enter AE data using a standardized and structured web-based data collection form. The automated AEDAMS forwards the AE information to individuals in designated roles (investigators, sponsors, Data Safety and Monitoring Boards) and manages subsequent communications in real time, as the entire reporting, review and notification is done by automatically generated emails. The system was designed to adhere to timelines and data requirements in compliance with Good Clinical Practice (International Conference on Harmonisation E6) reporting standards and US federal regulations, and can be configured to support AE management for many types of study designs and adhere to various domestic or international reporting requirements. This tool allows AEs to be collected in a standard way by multiple distributed users, facilitates accurate and timely AE reporting and reviews, and allows the centralized management of AEs. Our design justification and experience with the system are described.