The MSOAC approach to developing performance outcomes to measure and monitor multiple sclerosis disability

Background: The Multiple Sclerosis Outcome Assessments Consortium (MSOAC) was formed by the National MS Society to develop improved measures of multiple sclerosis (MS)-related disability. Objectives: (1) To assess the current literature and available data on functional performance outcome measures (PerfOs) and (2) to determine suitability of using PerfOs to quantify MS disability in MS clinical trials. Methods: (1) Identify disability dimensions common in MS; (2) conduct a comprehensive literature review of measures for those dimensions; (3) develop an MS Clinical Data Interchange Standards Consortium (CDISC) data standard; (4) create a database of standardized, pooled clinical trial data; (5) analyze the pooled data to assess psychometric properties of candidate measures; and (6) work with regulatory agencies to use the measures as primary or secondary outcomes in MS clinical trials. Conclusion: Considerable data exist supporting measures of the functional domains ambulation, manual dexterity, vision, and cognition. A CDISC standard for MS ( http://www.cdisc.org/therapeutic#MS ) was published, allowing pooling of clinical trial data. MSOAC member organizations contributed clinical data from 16 trials, including 14,370 subjects. Data from placebo-arm subjects are available to qualified researchers. This integrated, standardized dataset is being analyzed to support qualification of disability endpoints by regulatory agencies.

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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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Sharing data from MS clinical trials: Opportunities, challenges, and future directions

Multiple Sclerosis Journal ◽

10.1177/1352458515608005 ◽

2015 ◽

Vol 21 (11) ◽

pp. 1365-1368 ◽

Cited By ~ 2

Author(s):

Timothy Coetzee

Keyword(s):

Multiple Sclerosis ◽

Clinical Trial ◽

Clinical Trials ◽

Clinical Trial Data ◽

Therapeutic Agents ◽

Research Community ◽

Trial Data ◽

Fundamental Question ◽

Future Directions ◽

Phase Ii And Iii

The treatment of people affected by multiple sclerosis, particularly the relapsing forms of the disease, has been transformed by the availability of various therapeutic agents. This landmark progress is due to an enormous foundation of clinical research and, particularly, numerous phase II and III clinical trials. Although the research community has many reasons to take pride in this progress, a fundamental question remains about whether opportunities for additional research are being lost due to inadequate clinical trial data sharing.

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Mining Clinical Trial Data

Knowledge Discovery and Data Mining ◽

10.4018/978-1-59904-252-7.ch003 ◽

2011 ◽

pp. 31-47

Author(s):

Jose Ma. J. Alvir ◽

Javier Cabrera

Keyword(s):

Data Mining ◽

Clinical Trial ◽

Clinical Trials ◽

Clinical Trial Data ◽

Treated Group ◽

Trial Data ◽

Controlled Trials ◽

Clinical Trails ◽

Important Objective

Mining clinical trails is becoming an important tool for extracting information that might help design better clinical trials. One important objective is to identify characteristics of a subset of cases that responds substantially differently than the rest. For example, what are the characteristics of placebo respondents? Who have the best or worst response to a particular treatment? Are there subsets among the treated group who perform particularly well? In this chapter we give an overview of the processes of conducting clinical trials and the places where data mining might be of interest. We also introduce an algorithm for constructing data mining trees that are very useful for answering the above questions by detecting interesting features of the data. We illustrate the ARF method with an analysis of data from four placebo-controlled trials of ziprasidone in schizophrenia.

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The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis

Health and Quality of Life Outcomes ◽

10.1186/s12955-019-1220-z ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 12

Author(s):

Samantha Cruz Rivera ◽

Derek G. Kyte ◽

Olalekan Lee Aiyegbusi ◽

Anita L. Slade ◽

Christel McMullan ◽

...

Keyword(s):

Systematic Review ◽

Clinical Trial ◽

Clinical Trials ◽

Case Studies ◽

Real World ◽

Clinical Trial Data ◽

Research Impact ◽

Trial Data ◽

Patient Reported ◽

The Impact

Abstract Background Patient-reported outcomes (PROs) are commonly collected in clinical trials and should provide impactful evidence on the effect of interventions on patient symptoms and quality of life. However, it is unclear how PRO impact is currently realised in practice. In addition, the different types of impact associated with PRO trial results, their barriers and facilitators, and appropriate impact metrics are not well defined. Therefore, our objectives were: i) to determine the range of potential impacts from PRO clinical trial data, ii) identify potential PRO impact metrics and iii) identify barriers/facilitators to maximising PRO impact; and iv) to examine real-world evidence of PRO trial data impact based on Research Excellence Framework (REF) impact case studies. Methods Two independent investigators searched MEDLINE, EMBASE, CINAHL+, HMIC databases from inception until December 2018. Articles were eligible if they discussed research impact in the context of PRO clinical trial data. In addition, the REF 2014 database was systematically searched. REF impact case studies were included if they incorporated PRO data in a clinical trial. Results Thirty-nine publications of eleven thousand four hundred eighty screened met the inclusion criteria. Nine types of PRO trial impact were identified; the most frequent of which centred around PRO data informing clinical decision-making. The included publications identified several barriers and facilitators around PRO trial design, conduct, analysis and report that can hinder or promote the impact of PRO trial data. Sixty-nine out of two hundred nine screened REF 2014 case studies were included. 12 (17%) REF case studies led to demonstrable impact including changes to international guidelines; national guidelines; influencing cost-effectiveness analysis; and influencing drug approvals. Conclusions PRO trial data may potentially lead to a range of benefits for patients and society, which can be measured through appropriate impact metrics. However, in practice there is relatively limited evidence demonstrating directly attributable and indirect real world PRO-related research impact. In part, this is due to the wider challenges of measuring the impact of research and PRO-specific issues around design, conduct, analysis and reporting. Adherence to guidelines and multi-stakeholder collaboration is essential to maximise the use of PRO trial data, facilitate impact and minimise research waste. Trial registration Systematic Review registration PROSPERO CRD42017067799.

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The rate of tumor growth, g, as a biomarker for overall survival (OS) in prostate cancer (PC) in clinical trials as well as in real-world data from the Veterans Administration Medical Centers (VAMCs).

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.5074 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 5074-5074

Author(s):

Harshraj Leuva ◽

Mengxi Zhou ◽

Julia Wilkerson ◽

Keith Sigel ◽

Ta-Chueh Hsu ◽

...

Keyword(s):

Prostate Cancer ◽

Clinical Trial ◽

Overall Survival ◽

Clinical Trials ◽

Tumor Growth ◽

Real World ◽

Clinical Trial Data ◽

Trial Data ◽

Real World Data ◽

World Data

5074 Background: Novel assessments of efficacy are needed to improve determination of treatment outcomes in clinical trials and in real-world settings. Methods: Cancer treatments usually lead to concurrent regression and growth of the drug-sensitive and drug-resistant fractions of a tumor, respectively. We have exploited novel methods of analysis that assess these two simultaneous processes and have estimated rates of tumor growth ( g) and regression ( d) in over 30,000 patients (pts) with diverse tumors. Results: In prostate cancer (PC) we have analyzed both clinical trial and real-world data from Veterans. Using clinical trial data from 6819 pts enrolled in 15 treatment arms we have established separately and by combining all the data that g correlates highly (p<0.0001) with overall survival (OS) – slower g associated with better OS. In PC, abiraterone (ABI) and docetaxel (DOC) are superior to placebo, prednisone and mitoxantrone. ABI (median g =0.0017) is superior to DOC ( g=0.0021) in first line (p=0.0013); and ABI in 2nd line ( g=0.0034) is inferior to ABI in 1st line ( g=0.0017; p<0.0001). Finally, using combined clinical trial data as a benchmark we could assess the efficacy of novel therapies in as few as 30-40 patients. Amongst 7457 Veterans, the median g on a taxane ( g=0.0022) was similar to that from clinical trials ( g=0.0012). Although only 258 Veterans received cabazitaxel (CAB), g values for CAB ( g=0.0018) and DOC ( g=0.0023) were indistinguishable (p=0.3) consistent with their identical mechanism of action. Finally, outcomes with DOC in African American (AA) ( g=0.00212) and Caucasian ( g=0.00205) Veterans were indistinguishable (p=0.9) and comparable across all VAMCs. Conclusions: The rate of tumor growth, g, is an excellent biomarker for OS both in clinical trials and in real-world settings. g allows comparisons between trials and for large trial data sets to be used as benchmarks of efficacy. Real-world outcomes in the VAMCs are similar to those in clinical trials. In the egalitarian VAMCs DOC efficacy in PC is comparable in AA and Caucasian Veterans -- indicating inferior outcomes reported in AAs are likely due to differential health care access, not differences in biology.

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Transparency in clinical trials

10.24075/medet.2021.014 ◽

2021 ◽

Author(s):

ChS Pavlov ◽

DL Varganova ◽

AA Svistunov ◽

C Gluud

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Technology Development ◽

Personal Data ◽

Clinical Trial Data ◽

Trial Data ◽

Positive Effects ◽

The World ◽

Information Technology Development ◽

Meta Analyses

In the age of information technology development, healthcare professionals around the world have the opportunity to simultaneously access advanced scientific developments, modern achievements, and the results of new clinical trials. The clinical guidelines of the international medical communities are based on the results of meta-analyses of clinical trial data. As new medical challenges emerge, clinical trial data are reviewed and re-analyzed. Unfortunately, to date, the results of not all studies are made public, or are presented selectively, indicating the positive effects of a particular technology (intervention), which makes it difficult to critically evaluate the results of work and makes the task of assessing the true effectiveness of the intervention more difficult. The problem of transparency of research data with the preservation of personal data of participants remains relevant for decades. This article is focused on possible ways of solving this problem and the analysis of the current situation in the world.

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CLINICAL DATA MANAGEMENT IMPORTANCE IN CLINICAL RESEARCH

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2016.v9s2.13940 ◽

2016 ◽

pp. 59

Author(s):

Deepa Murugesan ◽

Ranganath Banerjee ◽

Gopal Ramesh Kumar

Keyword(s):

Clinical Trial ◽

Data Management ◽

Clinical Data ◽

Clinical Trial Data ◽

Drug Approval ◽

Trial Data ◽

Data Capture ◽

Data Management System ◽

Pharmaceutical Companies ◽

Management Tools

ABSTRACT Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and facilitates the drug discovery and development. Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.

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Dalteparin: A Low-Molecular-Weight Heparin

Annals of Pharmacotherapy ◽

10.1177/106002809703100212 ◽

1997 ◽

Vol 31 (2) ◽

pp. 192-203 ◽

Cited By ~ 27

Author(s):

Patricia A Howard

Keyword(s):

Clinical Trial ◽

Molecular Weight ◽

Clinical Trials ◽

Venous Thromboembolism ◽

Low Molecular Weight Heparin ◽

Clinical Trial Data ◽

Trial Data ◽

Fixed Dose ◽

Low Molecular Weight ◽

Review Articles

Objective To discuss the chemistry, pharmacology, and pharmacokinetics of dalteparin, a low-molecular-weight heparin (LMWH), and to review the comparative clinical trial data evaluating the efficacy and safety of dalteparin and unfractionated heparin (UH) for the prophylaxis and treatment of venous thromboembolism. Data Sources A MEDLINE search identified pertinent English-language publications on dalteparin and venous thromboembolism. Key search terms were dalteparin, Fragmin, LMWH, and venous thromboembolism. The search was supplemented by review articles, articles obtained from the bibliographies of the review articles, and the dalteparin approval database. Study Selection The most pertinent studies describing the pharmacology and pharmacokinetics of dalteparin in humans were selected; all abstracts and clinical trials evaluating the use of dalteparin for antithrombotic therapy were reviewed. Review articles by authors of international reputation were selected. Data Extraction Pertinent information from the review articles on the pharmacology of LMWHs and UH was summarized. Clinical trial data were extracted for study design, patient demographics, therapeutic regimens, methods of evaluation, and outcomes. Data Synthesis Dalteparin is an LMWH indicated for patients undergoing abdominal surgery who are considered to be at risk for deep-vein thrombosis (DVT), which may lead to pulmonary embolism (PE). In this population, numerous clinical trials have demonstrated comparable efficacy between dalteparin and fixed-dose UH for DVT prophylaxis. Dalteparin has a predictable dose response and can be administered as a standard single daily subcutaneous dose for all patients. In therapeutic doses, dalteparin does not alter coagulation tests and therefore does not require routine laboratory monitoring, in contrast with adjusted-dose UH. Bleeding risks with dalteparin are comparable with and possibly less than those associated with UH. Preliminary studies suggest that dalteparin may be effective for other indications, including DVT prophylaxis for hip replacement surgery and the treatment of DVT and PE. Comparative cost-effectiveness data are not yet available. Conclusions Dalteparin is the second LMWH to receive approval by the Food and Drug Administration. Dalteparin is indicated for prophylaxis against DVT in patients undergoing abdominal surgery. Clinical studies have shown that single daily doses of dalteparin provide a safe and effective alternative to fixed-dose UH therapy. Additional studies are needed to determine the cost-effectiveness of dalteparin compared with UH and other LMWHs.

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Esketamine clinical trials: reply to Maju et al.

Epidemiology and Psychiatric Sciences ◽

10.1017/s204579602000027x ◽

2020 ◽

Vol 29 ◽

Author(s):

C. Gastaldon ◽

D. Papola ◽

G. Ostuzzi ◽

C. Barbui

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Marketing Authorisation ◽

Clinical Trial Data ◽

Evidence Base ◽

Trial Data ◽

Regulatory Approval ◽

Controversial Issues

Abstract Maju et al. provided clarifications on important and controversial issues related to esketamine clinical trial data, in response to a vivid debate triggered by the marketing authorisation recently granted by this new medicine. In this commentary, we reply to their comments attempting to critically discuss the evidence base needed to obtain regulatory approval.

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