This data package contains datasets on clinical trials conducted in the United States. Diseases include cervical cancer, diabetes, acute respiratory infection as well as stress. This data package also includes clinical trials registry and results database.

Objective: To explore the structure and content of a non-random sample of clinical study reports (CSRs) to guide clinicians and systematic reviewers. Search strategy: We searched public sources and lodged Freedom of Information requests for previously confidential CSRs primarily written by industry for regulators. Selection criteria: CSR reporting sufficient information for extraction ("adequate"). Primary outcome measures: Presence and length of essential elements of trial design and reporting and compression factor (ratio of page length for CSR compared to its published counterpart in a scientific journal). Data extraction: data were extracted on standard forms and cross-checked for accuracy. Results: We assembled a population of 78 CSRs (covering 90 RCTs; 144,610 pages total) dated 1991-2011 of 14 pharmaceuticals. Report synopses had a median length of 5 pages, efficacy evaluation 13.5 pages, safety evaluation 17 pages, attached tables 337 pages, trial protocol 62 pages, statistical analysis plan 15 pages, and individual efficacy and safety listings had a median length of 447 and 109.5 pages, respectively. While 16 (21%) of CSRs contained completed case report forms, these were accessible to us in only one case (765 pages representing 16 individuals). Compression factors ranged between 1 and 8805. Conclusions: Clinical study reports represent a hitherto mostly hidden and untapped source of detailed and exhaustive data on each trial. They should be consulted by independent parties interested in a detailed record of a clinical trial, and should form the basic unit for evidence synthesis as their use is likely to minimize the problem of reporting bias. We cannot say whether our sample is representative and whether our conclusions are generalizable to an undefined and undefineable population of CSRs.

Clinical studies are an important part of drug development globally. The number of registered clinical trials has increased significantly recently. As of November 8, 2024, there were over 515 thousand clinical studies registered globally. The number of clinical studies has increased significantly since there were just 2,119 registered in 2000. In general, clinical trials have grown more complex in recent years and remain vital for the research and development of new drugs and products. Research and development Research and development are an important part of pharmaceutical companies and includes drug development and product development. Among all industry sectors, the pharmaceutical industry spends the largest percentage of their revenue on research and development. Many companies are active in pharmaceutical research and development globally. It is projected Swiss company Roche will remain one of the largest research and development spenders among pharmaceutical companies in the near future. Clinical studies globally Most clinical studies occurring globally are held in countries outside of the U.S. Many clinical trials performed outside the U.S. and EU are done so because it is often easier and cheaper to conduct trials in other locations. Success rates for clinical trials depend heavily on the stage of the trial and the drugs or products being developed. Recent data suggested that only around 29 percent of drugs make it from phase II to phase III.

On-line only tables. (DOCX)

clinicaltrials.gov_searchThis is complete original dataset.identify completed trialsThis is the R script which when run on "clinicaltrials.gov_search.txt" will produce a .csv file which lists all the completed trials.FDA_table_with_sensThis is the final dataset after cross referencing the trials. An explanation of the variables is included in the supplementary file "2011-10-31 Prayle Hurley Smyth Supplementary file 3 variables in the dataset".analysis_after_FDA_categorization_and_sensThis R script reproduces the analysis from the paper, including the tables and statistical tests. The comments should make it self explanatory.2011-11-02 prayle hurley smyth supplementary file 1 STROBE checklistThis is a STROBE checklist for the study2011-10-31 Prayle Hurley Smyth Supplementary file 2 examples of categorizationThis is a supplementary file which illustrates some of the decisions which had to be made when categorizing trials.2011-10-31 Prayle Hurley Smyth Supplementary file 3 variables in th...

The global HIV and AIDS clinical trials market size is expected to reach USD 7.7 billion by 2033, registering a CAGR of 3.5% during the forecast period (2025-2033), according to a new report by Grand View Research, Inc. The growing prevalence of HIV and AIDS is a major factor driving the market. The increasing number of clinical trials being conducted to develop new HIV and AIDS treatments and therapies is also contributing to the market's growth. Some of the key trends in the HIV and AIDS clinical trials market include:

The increasing use of innovative technologies, such as data mining and artificial intelligence, to optimize clinical trial design and outcomes. The growing trend towards conducting global clinical trials to increase patient recruitment and diversity. The growing emphasis on patient-centered care and outcomes in clinical trials.

The key players in the HIV and AIDS clinical trials market include PPD Inc., IQVIA Inc., Parexel International Corporation, ICON plc, Syneos Health, WuXi AppTec, Janssen Global Services, LLC, Gilead Sciences, Inc., Bionor Holding AS, Charles River Laboratories, GSK plc., and SGS SA. These companies offer a range of services for HIV and AIDS clinical trials, including clinical research services, data management services, and statistical analysis services.

This dataset includes all of the underlying data for our study, published in JAMA Internal Medicine (JAMA Intern Med. 2017;177(10):1452-1459. doi:10.1001/jamainternmed.2017.3820), along with our extraction sheets and work files.

Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets.
Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning.
Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools’ increasing popularity can be attributed to features like sophisticated modeling and predictive analytics.
A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential.
Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software.
Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques.
Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this.
Big Data Analytics’s Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data.
Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities.
Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector.
Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

aThis table summarizes data presented in Tables 4 and 5 and Text S1's table 1. Along the top row in this table, we show every type of efficacy analysis described in the protocols, SAPs, and publications across all nine trials for which we compared these documents. The first column on the left lists the criteria used to define the types of analysis across all studies. For each type of analysis, an “X” denotes that the criterion was applied in at least one of the documents for any of the nine trials we examined. For example, the second column summarizes the five criteria used across all documents and trials to define ITT: in Table 4, four criteria were used in different combinations to define ITT analysis; in Text S1's table 1, one additional criterion was used in the SAP.bThis type of analysis was specified protocols, SAPs, and publications for the trials we examined (Tables 4 and 5 and Text S1's table 1).cThis type of analysis was specified only in the protocol and publications for some of the trials we examined (see Table 5).dThis type of analysis specified only in SAPs for some of the trials we examined (see Text S1's table 1).CGIS, clinical global impression of severity; HAM-D, Hamilton Depression Rating Scale; YMRS, Young Mania Rating Scale.

Clinical Trial Management System Market size was valued at USD 1009.73 Million in 2023 and is projected to reach USD 2310.23 Million by 2031, growing at a CAGR of 10.90% from 2024 to 2031.

Clinical Trial Management Market: Definition/ Overview

Clinical trials are meticulously overseen through a process known as clinical trial management. This process ensures the safety and efficacy of new medical interventions in human subjects by planning, organizing, conducting, monitoring, analyzing, and reporting on clinical trials. Strict regulations govern the entire process to safeguard participants and guarantee the integrity of the collected data. The clinical trial management journey begins with pre-trial activities. A detailed protocol outlining the study design, participant selection criteria, procedures, data collection methods, and statistical analysis plan is first developed. This protocol is then submitted to regulatory bodies for review and approval. Research sites with qualified investigators and staff are identified and trained on the protocol once approval is granted. Additionally, financial resources required for the trial are estimated and contracts are established with involved parties.

Objective: To determine rates of publication and reporting of results within two years for all completed clinical trials registered in ClinicalTrials.gov across leading academic medical centers in the United States. Design: Cross sectional analysis. Setting: Academic medical centers in the United States. Participants: Academic medical centers with 40 or more completed interventional trials registered on ClinicalTrials.gov. Methods: Using the Aggregate Analysis of ClinicalTrials.gov database and manual review, we identified all interventional clinical trials registered on ClinicalTrials.gov with a primary completion date between October 2007 and September 2010 and with a lead investigator affiliated with an academic medical center. Main outcome measures: The proportion of trials that disseminated results, defined as publication or reporting of results on ClinicalTrials.gov, overall and within 24 months of study completion. Results: We identified 4347 interventional clinical trials across 51 academic medical centers. Among the trials, 1005 (23%) enrolled more than 100 patients, 1216 (28%) were double blind, and 2169 (50%) were phase II through IV. Overall, academic medical centers disseminated results for 2892 (66%) trials, with 1560 (35.9%) achieving this within 24 months of study completion. The proportion of clinical trials with results disseminated within 24 months of study completion ranged from 16.2% (6/37) to 55.3% (57/103) across academic medical centers. The proportion of clinical trials published within 24 months of study completion ranged from 10.8% (4/37) to 40.3% (31/77) across academic medical centers, whereas results reporting on ClinicalTrials.gov ranged from 1.6% (2/122) to 40.7% (72/177). Conclusions: Despite the ethical mandate and expressed values and mission of academic institutions, there is poor performance and noticeable variation in the dissemination of clinical trial results across leading academic medical centers.

CTMS products vary in functionality and capabilities, catering to different trial types and organizational needs. Key product offerings include:Electronic Data Capture (EDC): Captures and manages clinical trial data from various sources, ensuring data accuracy and compliance.Interactive Response Technology (IRT): Automates patient randomization, drug dispensing, and treatment management, improving trial efficiency and patient safety.Patient Relationship Management (PRM): Manages patient recruitment, engagement, and retention throughout the trial process.Clinical Trial Supply Management (CTSM): Optimizes inventory management, tracking, and distribution of investigational products, ensuring timely and accurate delivery.Data Management and Analysis: Provides data visualization, statistical analysis, and reporting capabilities to track trial progress and identify trends. Recent developments include: , June 2022: Medidata Solutions(US), a Dassault Systèmes company, announced technological improvements to solve challenges in clinical trial management. It will improve data oversight and reporting for contract research organizations (CROs) and sponsors, March 2020: Medidata Solutions(US) launched a new program to support research organizations conducting any type of immunology-related research, including clinical studies on COVID-19 vaccines, diagnostics, and treatments., February 2021: eClinical Solutions LLC(India), a provider of cloud-based software, launched the elluminate clinical trial management system that accelerates digitalization and shortens cycle times for clinical data review., June 2019: Parexel International Corporation. (US) launched an integrated outsourcing delivery model for a functional service provider (FSP) services at the DIA 2019 global annual meeting in San Diego(US)., January 2021: Bioclinica (US) partnered with IKCON PHARMA(India) to Deliver Best-in-Class eClinical Solutions to Pharma Industry/Sponsors. As part of the partnership, IKCON PHARMA will have access to Bioclinica's Interactive Response Technology (IRT) and Electronic Data Capture (EDC) platform to help its clients execute and maintain clinical procedures..

Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

Objectives: To develop and pilot a tool to measure and improve pharmaceutical companies' clinical trial data sharing policies and practices. Design: Cross sectional descriptive analysis. Setting: Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources: Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures: Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results: Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions: It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study's measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

PRISMA Checklist. (DOC)

The probability that a cancer drug will make it from phase I to final approval stood at some 2.4 percent, according to a study published in March 2020. The data is based on clinical success rate models from 2014, 2016, and 2019. Once a cancer drug made it into phase III, the chances to make it to final approval increased to 35.5 percent.

This metadata describes the data format for data contributions to the International COVID-19 Data Alliance (ICODA) driver project investigating the safety and efficacy of clinical trials. The first data dictionary was published in December 2020, newer versions are available.

Several thousand clinical COVID-19 trials were in progress globally. As these trials were all evaluating the benefit/risk of potential COVID-19 treatment options, it was vital that the scientific community could interrogate this data as it emerged.

The summary level data from some of these trials across industry, academia and government was included in the ICODA Workbench. In order to provide near-immediate access to results and data from the trials, ICODA has partnered with Certara to provide curated and digitised summary level data from key trials as they were reported in the public domain. In addition, several data contributing organisations provided enriched summary-level data within 5-30 days post top-line reporting of the trial results which allowed a more in depth evaluation of the results.

This Driver project used a Data Dictionary to harmonise variable definitions and subgroup classifications from all trials. This allowed side by side interrogation of the data from these trials making the data readily useable to interpret findings. Researchers could also view data from individual trials in the context of other available trials thus expanding their insights. Our visual analytics and meta-analyses tools further enhanced the researchers’ ability to work quickly.

This statistic shows the total per-study costs by clinical trial phase and therapeutic area as of 2014, in million U.S. dollars. The clinical trial phase IV for studies of the respiratory system was the most expensive phase, with a total per-study cost of nearly 73 million U.S. dollars.

BackgroundIn medical practice, clinically unexpected measurements might be quite properly handled by the remeasurement, removal, or reclassification of patients. If these habits are not prevented during clinical research, how much of each is needed to sway an entire study?Methods and ResultsBelieving there is a difference between groups, a well-intentioned clinician researcher addresses unexpected values. We tested how much removal, remeasurement, or reclassification of patients would be needed in most cases to turn an otherwise-neutral study positive. Remeasurement of 19 patients out of 200 per group was required to make most studies positive. Removal was more powerful: just 9 out of 200 was enough. Reclassification was most powerful, with 5 out of 200 enough. The larger the study, the smaller the proportion of patients needing to be manipulated to make the study positive: the percentages needed to be remeasured, removed, or reclassified fell from 45%, 20%, and 10% respectively for a 20 patient-per-group study, to 4%, 2%, and 1% for an 800 patient-per-group study. Dot-plots, but not bar-charts, make the perhaps-inadvertent manipulations visible. Detection is possible using statistical methods such as the Tadpole test.ConclusionsBehaviours necessary for clinical practice are destructive to clinical research. Even small amounts of selective remeasurement, removal, or reclassification can produce false positive results. Size matters: larger studies are proportionately more vulnerable. If observational studies permit selective unblinded enrolment, malleable classification, or selective remeasurement, then results are not credible. Clinical research is very vulnerable to “remeasurement, removal, and reclassification”, the 3 evil R's.

Global Clinical Trials Market Size Was Worth USD 53.92 Billion in 2022 and Is Expected to Reach USD 85.01 Billion by 2030, CAGR of 5.93%