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.

This statistic shows the number of registered clinical studies worldwide by location, as of June 13, 2025. The number of registered clinical studies in non-U.S. areas was at around 303 thousand, while in the U.S. the number was at around 159 thousand.

This statistic shows the average length of a clinical trial cycle from 2020 to 2024, in months. The data is based on the top 20 biopharma companies by R&D spend. As can be seen, the average length of trials increased, with a peak of over 100 months in 2024.

The National Database for Clinical Trials Related to Mental Illness (NDCT) is an extensible informatics platform for relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.) related to clinical trials funded by the National Institute of Mental Health. Sharing data, associated tools, methodologies and results, rather than just summaries or interpretations, accelerates research progress. Community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches for the sharing of de-identified human subject research data. Built on the National Database for Autism Research (NDAR) informatics platform, NDCT provides a comprehensive data sharing platform for NIMH grantees supporting clinical trials.

This is synthetic patient-level clinical trial data, re-created based on data from a clinical trial for corticosteroids and antiviral agents as treatment for Bell's Palsy: https://www.nejm.org/doi/full/10.1056/nejmoa072006#

Bell's Palsy is a sudden, temporary weakness or paralysis of the muscles on one side of the face. The exact cause is unknown, but it's believed to occur due to swelling and inflammation of the nerve that controls the muscles on one side of the face, which can be triggered by a viral infection.

The authors conducted a double-blind, placebo-controlled, randomized, factorial trial involving patients with Bell's Palsy who were recruited within 72 hours after the onset of symptoms. Patients were randomly assigned to receive 10 days of treatment with prednisolone, acyclovir, both agents, or placebo. The primary outcome was recovery of facial function, as rated on the House–Brackmann scale.

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Research dataset and analysis for Clinical Trials including statistics, forecasts, and market insights

The goal of the Clinical Trials track is to focus research on the clinical trials matching problem: given a free text summary of a patient health record, find suitable clinical trials for that patient.

In the period from 2018 to 2023, 28 percent of clinical drug trials started during these years were in the field of oncology, whereas a combined 13 percent of trials started were for infectious diseases (therapeutic and immunization). This statistic depicts the proportion of clinical trials started worldwide from 2018 to 2023, by key therapeutic area.

Clinical Trial Data Repository Market Outlook

The global clinical trial data repository market size was estimated to be approximately $1.8 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 9.5% to reach around $4.1 billion by 2032. The primary growth factors include the increasing volume and complexity of clinical trials, rising need for efficient data management systems, and stringent regulatory requirements for data accuracy and integrity. The advent of advanced technologies such as artificial intelligence and big data analytics further drives market expansion by enhancing data processing capabilities and providing actionable insights.

The growth of the clinical trial data repository market is significantly influenced by the increasing number of clinical trials being conducted globally. With the rise in chronic diseases, the need for innovative treatments and therapies has surged, leading to an upsurge in clinical trials. This increase in clinical trials necessitates robust data management systems to handle vast amounts of data generated, thereby propelling the demand for clinical trial data repositories. Moreover, the complexity of modern clinical trials, which often involve multiple sites and diverse patient populations, further amplifies the need for sophisticated data management solutions.

Another critical driver for the market is the stringent regulatory landscape governing clinical trial data. Regulatory bodies such as the FDA, EMA, and other local authorities mandate rigorous data management standards to ensure data integrity, accuracy, and accessibility. These regulations necessitate the adoption of advanced data repository systems that can comply with regulatory requirements, thereby fueling market growth. Additionally, regulatory frameworks are becoming increasingly stringent, prompting pharmaceutical and biotechnology companies to invest in state-of-the-art data management systems to avoid compliance issues and potential financial penalties.

Technological advancements play a pivotal role in the market's growth. The integration of artificial intelligence, machine learning, and big data analytics into data repository systems enhances data processing and analysis capabilities. These technologies enable real-time data monitoring, predictive analytics, and improved decision-making, thereby improving the efficiency of clinical trials. Furthermore, the shift towards cloud-based solutions offers scalability, flexibility, and cost-effectiveness, making advanced data management systems accessible to even small and medium-sized enterprises.

Regionally, North America dominates the clinical trial data repository market owing to its robust healthcare infrastructure, high R&D investments, and presence of major pharmaceutical and biotechnology companies. Europe follows closely due to stringent regulatory standards and a strong focus on clinical research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to increasing clinical trial activities, growing healthcare expenditure, and the rising adoption of advanced technologies. Latin America and the Middle East & Africa are also likely to experience growth, albeit at a slower pace, driven by improving healthcare systems and increasing focus on clinical research.

Component Analysis

The clinical trial data repository market is segmented by components into software and services. The software segment is anticipated to hold a significant share of the market due to the essential role software plays in data management. Advanced software solutions offer capabilities such as data storage, management, retrieval, and analysis, which are critical for effective clinical trial management. The integration of AI and machine learning algorithms into these software systems further enhances their efficiency by enabling predictive analytics and real-time monitoring, thus driving the software segment's growth.

Software solutions in clinical trial data repositories also offer interoperability, enabling seamless integration with other clinical trial management systems (CTMS) and electronic data capture (EDC) systems. This interoperability is crucial for ensuring data consistency and accuracy across different platforms, thereby enhancing overall data management. Additionally, the increasing adoption of cloud-based software solutions provides scalability, cost-effectiveness, and remote acce

Overview: The AIDS Clinical Trials Group Study 175 Dataset, initially published in 1996, is a comprehensive collection of healthcare statistics and categorical information about patients diagnosed with AIDS. This dataset was created with the primary purpose of examining the performance of two different types of AIDS treatments: zidovudine (AZT) versus didanosine (ddI), AZT plus ddI, and AZT plus zalcitabine (ddC). The prediction task associated with this dataset involves determining whether each patient died within a specified time window.

Dataset Details: - Number of rows: 2139 - Number of columns: 24

Purpose of Dataset Creation: The dataset was created to evaluate the efficacy and safety of various AIDS treatments, specifically comparing the performance of AZT, ddI, and ddC in preventing disease progression in HIV-infected patients with CD4 counts ranging from 200 to 500 cells/mm3. This intervention trial aimed to contribute insights into the effectiveness of monotherapy versus combination therapy with nucleoside analogs.

Funding Sources: The creation of this dataset was funded by: - AIDS Clinical Trials Group of the National Institute of Allergy and Infectious Diseases - General Research Center units funded by the National Center for Research Resources

Instance Representation: Each instance in the dataset represents a health record of a patient diagnosed with AIDS in the United States. These records encompass crucial categorical information and healthcare statistics related to the patient's condition.

Study Design: - Study Type: Interventional (Clinical Trial) - Enrollment: 2100 participants - Masking: Double-Blind - Primary Purpose: Treatment - Official Title: A Randomized, Double-Blind Phase II/III Trial of Monotherapy vs. Combination Therapy With Nucleoside Analogs in HIV-Infected Persons With CD4 Cells of 200-500/mm3 - Study Completion Date: November 1995

Study Objectives: To determine the effectiveness and safety of different AIDS treatments, including AZT, ddI, and ddC, in preventing disease progression among HIV-infected patients with specific CD4 cell counts.

Additional Information: The dataset provides valuable insights into the HIV-related clinical trials conducted by the AIDS Clinical Trials Group, contributing to the understanding of treatment outcomes and informing future research in the field.

Attributes Description:

1. Patient Information:

2. Censoring Indicator (label):Binary indicator (1 = failure, 0 = censoring) denoting patient status.

3. Temporal Information:

4. Time to Event (time): Integer representing time to failure or censoring.

5. Treatment Features:

   • Treatment Indicator (trt): Categorical feature indicating the type of treatment received (0 = ZDV only, 1 = ZDV + ddI, 2 = ZDV + Zal, 3 = ddI only).

6. Baseline Health Metrics:

7. Age (age): Patient's age in years at baseline.

8. Weight (wtkg): Continuous feature representing weight in kilograms at baseline.

9. Hemophilia (hemo): Binary indicator of hemophilia status (0 = no, 1 = yes).

10. Sexual Orientation (homo): Binary indicator of homosexual activity (0 = no, 1 = yes).

11. IV Drug Use History (drugs): Binary indicator of history of IV drug use (0 = no, 1 = yes).

12. Karnofsky Score (karnof): Integer on a scale of 0-100 indicating the patient's functional status.

13. Antiretroviral Therapy History:

14. Non-ZDV Antiretroviral Therapy Pre-175 (oprior): Binary indicator of non-ZDV antiretroviral therapy pre-Study 175 (0 = no, 1 = yes).

15. ZDV in the 30 Days Prior to 175 (z30): Binary indicator of ZDV use in the 30 days prior to Study 175 (0 = no, 1 = yes).

16. ZDV Prior to 175 (zprior): Binary indicator of ZDV use prior to Study 175 (0 = no, 1 = yes).

17. Days Pre-175 Anti-Retroviral Therapy (preanti): Integer representing the number of days of pre-Study 175 anti-retroviral therapy.

18. Demographic Information:

19. Race (race): Integer denoting race (0 = White, 1 = non-white).

20. Gender (gender): Binary indicator of gender (0 = Female, 1 = Male).

21. Treatment History:

22. Antiretroviral History (str2): Binary indicator of antiretroviral history (0 = naive, 1 = experienced).

23. Antiretroviral History Stratification (strat): Integer representing antiretroviral history stratification.

24. Symptomatic Information:

25. Symptomatic Indicator (symptom): Binary indicator of symptomatic status (0 = asymptomatic, 1 = symptomatic).

26. Additional Treatment Attributes:

27. Treatment Indicator (treat): Binary indicator of treatment (0 = ZDV only, 1 = others).

28. Off-Treatment Indicator (offtrt): Binary indicator of being off-treatment before 96+/-5 weeks (0 = no, 1 = yes).

29. Immunological Metrics:

30. CD4 Counts (cd40, cd420): Integer values representing CD4 counts at baseline and 20+/-5 weeks.

31. CD8 Counts (cd80, cd820): Integer values representing CD8 counts at baseline and 20+/-5 weeks.

Original Dataset Website: [h...

Clinical trials of drugs mentioning AI have grown leaps and bounds since 2016, growing by around ***** percent year-on-year, a staggering jump. This has some ethical considerations as AI is not yet fully tested as a technology and the degree to which AI is included in each trial will have a significant impact on its validity.

The Clinical Research Information Service (CRIS) is a public data service that integrates and manages information on clinical trials conducted in Korea. These statistics provide a comprehensive overview of research based on clinical trial information registered in CRIS. Key statistical data include study type, intervention type, disease classification, clinical trial phase, research results, medical benefits, and the name of the responsible institution. This allows for a systematic understanding of the status and trends of domestic clinical trials, serving as fundamental data for research policy development and the advancement of healthcare research. Furthermore, by enhancing the transparency and reliability of research information, it supports the public's understanding of the current state of clinical research.

Background Acceptability curves have been proposed for quantifying the probability that a treatment under investigation in a clinical trial is cost-effective. Various definitions and estimation methods have been proposed. Loosely speaking, all the definitions, Bayesian or otherwise, relate to the probability that the treatment under consideration is cost-effective as a function of the value placed on a unit of effectiveness. These definitions are, in fact, expressions of the certainty with which the current evidence would lead us to believe that the treatment under consideration is cost-effective, and are dependent on the amount of evidence (i.e. sample size). Methods An alternative for quantifying the probability that the treatment under consideration is cost-effective, which is independent of sample size, is proposed. Results Non-parametric methods are given for point and interval estimation. In addition, these methods provide a non-parametric estimator and confidence interval for the incremental cost-effectiveness ratio. An example is provided. Conclusions The proposed parameter for quantifying the probability that a new therapy is cost-effective is superior to the acceptability curve because it is not sample size dependent and because it can be interpreted as the proportion of patients who would benefit if given the new therapy. Non-parametric methods are used to estimate the parameter and its variance, providing the appropriate confidence intervals and test of hypothesis.

Clinical Trials

Clinical trials over the years along with start / end dates, outcome and more.

By Aero Data Lab [source]

About this dataset

This dataset contains information on clinical trials conducted by sponsors. Each row represents a clinical trial, and the columns represent various attributes of the trial, such as the National Clinical Trial Number, the sponsor of the trial, the title of the trial, and so on.

The purpose of this dataset is to provide a bird's-eye view of the clinical trial landscape. By understanding which sponsors are conducting which trials and for what conditions, we can get a better sense of where research is headed and what new treatments may be on the horizon

How to use the dataset

• NCT is a unique identifier for clinical trials. It stands for National Clinical Trial Number.
• Sponsor is the organization that is funding the clinical trial.
• Title is the name of the clinical trial.
• Summary is a brief summary of the clinical trial.
• Start Year is the year that the clinical trial started.
• Start Month is the month that the clinical trial started.
• Phase is the stage of development of the investigative drug or device (I), which can be one of four types: I, II, III, or IV.
• Enrollment is The number of participants in the clinical trial.
• Status is The status of enrollment in the study, which can be Recruiting, Not yet recruiting, Active, not recruiting, Completed, Suspended, or Terminated.

Condition indicates what medical condition(s) are being studied in this particular NCT record

Research Ideas

• Identify patterns in clinical trials to improve the development process
• Understand how different sponsors fund clinical trials

Acknowledgements

By Aero Data Lab [source]

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: AERO-BirdsEye-Data.csv | Column name | Description | |:----------------|:-----------------------------------------------------------------| | NCT | National Clinical Trial number. (String) | | Sponsor | Name of the sponsor conducting the clinical trial. (String) | | Title | Title of the clinical trial. (String) | | Summary | Brief summary of the clinical trial. (String) | | Start_Year | Year the clinical trial started. (Integer) | | Start_Month | Month the clinical trial started. (String) | | Phase | Phase of the clinical trial. (String) | | Enrollment | Number of participants enrolled in the clinical trial. (Integer) | | Status | Status of the clinical trial. (String) | | Condition | Condition being tested in the clinical trial. (String) |

Acknowledgements

If you use this dataset in your research, please credit By Aero Data Lab [source]

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...

Clinical trials efficacy results (csv)

Data set of the service quality and satisfaction in clinical trials.

A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.

This dataset provides comprehensive information on clinical trial research sites, including site identification, principal investigator details, enrollment statistics, audit and compliance history, and performance metrics. It is designed to support multi-center trial management, site selection, regulatory oversight, and operational optimization for clinical research organizations.

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Clinical Trial Support Services Market Size 2025-2029

The clinical trial support services market size is valued to increase USD 8.92 billion, at a CAGR of 7.4% from 2024 to 2029. Growth of biopharmaceutical industry will drive the clinical trial support services market.

Major Market Trends & Insights

Asia dominated the market and accounted for a 33% growth during the forecast period.
By Application - Phase 2 segment was valued at USD 6.16 billion in 2023
By Age Group - Adults (greater than 18 years) segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 71.60 million
Market Future Opportunities: USD 8916.10 million
CAGR : 7.4%
Asia: Largest market in 2023

Market Summary

The market encompasses a range of technologies, applications, and services that facilitate the successful execution of clinical trials. Core technologies, such as electronic data capture (EDC) and interactive response technology (IRT), streamline data collection and management. Applications include pharmacovigilance, biostatistics, and data management. Service types include contract research organizations (CROs), clinical trial supplies, and site management. The market's evolution is driven by the growing demand for CROs due to the high cost of clinical trials and the need for specialized expertise. According to a report by Global Market Insights, the CRO market is projected to reach a market share of over 50% by 2026.
Regulations, such as the International Conference on Harmonization (ICH) guidelines, also play a significant role in shaping the market. Despite these opportunities, challenges persist, including data security concerns, complex regulatory requirements, and the need for standardization. The market's continuous unfolding is influenced by the growth of the biopharmaceutical industry, with increasing investment in research and development, and the ongoing trend towards personalized medicine. As the market evolves, stakeholders must remain agile and adapt to emerging trends and technologies to stay competitive.

What will be the Size of the Clinical Trial Support Services Market during the forecast period?

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How is the Clinical Trial Support Services Market Segmented and what are the key trends of market segmentation?

The clinical trial support services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Phase 2
  Phase 3
  Phase 1
  Phase 4


Age Group

  Adults (greater than 18 years)
  Adolescents (10 to 18 years)
  Children (less than 10 years)


Therapeutic Area

  Oncology
  Cardiology
  Neurology
  Infectious diseases
  Others


Geography

  North America

    US


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The phase 2 segment is estimated to witness significant growth during the forecast period.

In the intermediate stage of clinical research, Phase 2 of the market plays a pivotal role. This phase focuses on evaluating the efficacy and side effects of a new treatment in a larger patient population. Key services provided during this phase include site selection and management, patient recruitment strategies, regulatory compliance support, pharmacovigilance services, electronic data capture, and data management systems. Robust data collection and analysis are essential to ensure accurate results. For instance, IQVIA's One Home for Sites, launched in June 2024, is a unified clinical trial technology platform designed to simplify and streamline research site operations.

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The Phase 2 segment was valued at USD 6.16 billion in 2019 and showed a gradual increase during the forecast period.

This platform aims to improve operational efficiency and data quality. Patient engagement strategies, data privacy regulations, and centralized laboratory services are other critical components of Phase 2. Patient feedback mechanisms, biometric data collection, and clinical trial monitoring are also essential services. Moreover, ePro solutions, statistical analysis planning, data validation techniques, and study start-up support are integral to the success of this phase. Furthermore, remote patient monitoring, interactive voice response, randomization and stratification, medical device testing, eConsent platforms, safety reporting systems, investigator recruitment, and independent data monitoring are additional services that contribute to the ongoing evolution of Phase 2. The market for clinical trial support services is expected to grow signif