Statistical center data management in multicenter clinical trials

We would like to introduce you to a new section in Advances in Pulmonary Hypertension in which we highlight results from ongoing and recent clinical trials. The preliminary results of several multicenter clinical trials have recently been presented. In this issue, we will focus on the results of Freedom-C, which was presented in November of 2008, as well as the Walk-PHaSST study, which was stopped early in July 2009.

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Clinical Trial Data Management Software: A Review of the Technical Features

Reviews on Recent Clinical Trials ◽

10.2174/1574887114666190207151500 ◽

2019 ◽

Vol 14 (3) ◽

pp. 160-172 ◽

Cited By ~ 1

Author(s):

Aynaz Nourani ◽

Haleh Ayatollahi ◽

Masoud Solaymani Dodaran

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Data Management ◽

Clinical Data ◽

Clinical Trial Data ◽

Trial Data ◽

Data Management System ◽

Management Systems ◽

Data Management Systems ◽

Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

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A Review of Clinical Data Management Systems Used in Clinical Trials

Reviews on Recent Clinical Trials ◽

10.2174/1574887113666180924165230 ◽

2019 ◽

Vol 14 (1) ◽

pp. 10-23 ◽

Cited By ~ 1

Author(s):

Aynaz Nourani ◽

Haleh Ayatollahi ◽

Masoud Solaymani Dodaran

Keyword(s):

Clinical Trials ◽

Data Management ◽

Clinical Data ◽

Data Management System ◽

Management Systems ◽

Management Process ◽

Data Management Systems ◽

Ovid Medline ◽

Research Findings ◽

Accuracy Of Results

Background:A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.Methods:This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.Results:The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.Conclusion:Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

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Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Trials ◽

10.1186/s13063-021-05141-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Sophie Relph ◽

◽

Maria Elstad ◽

Bolaji Coker ◽

Matias C. Vieira ◽

...

Keyword(s):

Clinical Trials ◽

Data Quality ◽

Data Management ◽

Randomised Control Trial ◽

Data Dictionary ◽

Patient Records ◽

Electronic Patient Records ◽

Cluster Randomised ◽

Quality Checks ◽

Data Harmonisation

Abstract Background The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. Methods The DESiGN Trial is a cluster randomised control trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age foetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from foetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. Results Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites; each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1–4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0–1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. Conclusions Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units. In this manuscript, we describe how we managed this and provide a detailed data dictionary covering the harmonisation of variable names and values that will be helpful for other researchers working with these data. Trial registration Primary registry and trial identifying number: ISRCTN 67698474. Registered on 02/11/16.

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