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.

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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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.

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.

This statistic shows the percentage of clinical studies with posted results worldwide by type, as of June 13, 2025. Some 94 percent of studies with posted results were interventional types.

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US Clinical Trials Market Size 2025-2029

The us clinical trials market size is valued to increase USD 6.5 billion, at a CAGR of 5.3% from 2024 to 2029. Rise in number of clinical trials of drugs will drive the us clinical trials market.

Major Market Trends & Insights

By Type - Phase III segment was valued at USD 9.50 billion in 2022
By Service Type - Interventional studies segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 61.02 billion
Market Future Opportunities: USD 6.50 billion
CAGR from 2024 to 2029 : 5.3%

Market Summary

The Clinical Trials Market in the US is a dynamic and evolving landscape shaped by advancements in core technologies and applications, service types, and regulatory frameworks. With the rise in the number of clinical trials for drugs, the market is witnessing significant growth. According to a recent report, the adoption rate of electronic data capture (EDC) systems in clinical trials has surged to over 70%, revolutionizing data management and analysis. However, the increasing cost of clinical trials poses a major challenge for market participants. In 2020, the average cost of a Phase III trial was estimated to be around USD4.5 billion. Despite these challenges, opportunities abound, particularly in areas such as personalized medicine and remote patient monitoring. As technology and scientific research continue to advance, the Clinical Trials Market in the US remains an exciting and innovative space.

What will be the Size of the US Clinical Trials Market during the forecast period?

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How is the Clinical Trials in US Market Segmented and what are the key trends of market segmentation?

The clinical trials in us industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypePhase IIIPhase IPhase IIPhase IVService TypeInterventional studiesObservational studiesExpanded access studiesIndicationOncologyCNSAutoimmune/inflammationOthersGeographyNorth AmericaUS

By Type Insights

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

The clinical trials market in the US is a dynamic and evolving landscape, with ongoing activities and emerging patterns shaping the drug development process. Phase 3 trials, a crucial segment, assess the safety and efficacy of new drugs or treatments on larger patient populations. In April 2024, the FDA granted accelerated approval to Enhertu for adult patients with unresectable or metastatic HER2-positive solid tumors who have previously undergone systemic treatment. This approval underscores Enhertu's potential to address a significant unmet need, solidifying its role in the market. Throughout the clinical trial process, from protocol development and sample size calculation to patient recruitment, informed consent, and adverse event reporting, regulatory compliance is paramount. Technological advancements, such as electronic health records, remote patient monitoring, and eCRF systems, facilitate more efficient data collection and management. Study design, including blinded, placebo-controlled, and parallel group trials, ensures rigorous testing and unbiased results. Adaptive clinical trials allow for real-time data analysis and adjustments, enhancing trial efficiency. Key aspects, like clinical data management, biomarker identification, and statistical analysis plans, ensure data integrity and standardization. Investigator training, interim analysis, and trial monitoring maintain study quality and regulatory compliance. With a focus on data privacy and security, the clinical trials market continues to evolve, addressing the needs of patients and stakeholders alike.

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The Phase III segment was valued at USD 9.50 billion in 2019 and showed a gradual increase during the forecast period.

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Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

The clinical trials market in the US is witnessing significant advancements, driven by the adoption of innovative technologies and strategies to streamline trial processes and enhance patient engagement. One such technology, the clinical trial data management system, is gaining traction due to its ability to facilitate efficient data collection, processing, and reporting. This system integrates various tools such as remote patient monitoring technology, electronic case report forms (eCRFs), and clinical trial data visualization too

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.

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

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

As of February 2025, there were a total of ***** clinical studies performed in Peru. By status, the vast majority corresponded to completed studies, which amounted to *** clinical trials, representing nearly ** percent of the total. Meanwhile, 100 clinical studies were recruiting participants, and *** medical trials were terminated or finished earlier. Peru ranked sixth among the countries in Latin America with the highest number of clinical trials in 2024.

Worldwide, the total number of clinical studies with posted results was ***** at year-end 2009 and increased up to over ** thousand as of June 13, 2025. This statistic shows the total number of registered clinical studies with posted results worldwide since 2008.

Clinical trials efficacy results (csv)

As of June 13, 2025, interventional types of studies made up 76 percent of the total number of registered clinical studies. This statistic shows the percentage of registered clinical studies worldwide by type.

Analysis of subgroup results in a clinical trial is surprisingly unreliable, even in a large trial. This is the result of a combination of reduced statistical power, increased variance and the play of chance. Reliance on such analyses is likely to be more erroneous, and hence harmful, than application of the overall proportional (or relative) result in the whole trial to the estimate of absolute risk in that subgroup. Plausible explanations can usually be found for effects that are, in reality, simply due to the play of chance. When clinicians believe such subgroup analyses, there is a real danger of harm to the individual patient.

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 Trial Management Systems (CTMS) maintain and manage the planning, preparation, performance and reporting of clinical trials, with emphasis on tracking participants, deadlines and milestones. These systems are used by pharmaceutical and life sciences companies to manage large amounts of data involved in the operation of a clinical trial. CTMS help to identify key performance indicators, while establishing metrics, results and performance management.

According to the Cognitive Market Research Report, the clinical trials Market size in 2024 was XX Million and is projected to have a compounded annual growth rate of XX% from 2024 to 2031.

The fast advancements in precision medicine and the growing demand for individualized therapies will necessitate the development of a more flexible and agile supply chain in the clinical trial industry.
The increased need for innovative treatments and cures is driving the rapidly expanding clinical trial industry. Several companies in this sector provide clinical trial services. 
 The Phase III segment dominated the market. The growing number of phase III trials with numerous participants is driving the market growth, according to clinical trial statistics. A greater number of patients and frequently a longer treatment duration are also necessary for phase III. 
North America became the world's largest market for clinical trials in 2023, accounting for XX million of the market revenue and nearly XX% of the market share. The market is anticipated to grow for several significant reasons, including an increase in clinical studies and growing R&D expenses for the pharmaceutical and biopharmaceutical industries.

Market Dynamics of Clinical Trials Market

Key Drivers of the clinical trials market

New Technology Adoption in Clinical Research

A dramatic change in the clinical trial landscape has been brought about by the use of new technology in clinical research, greatly accelerating the market's growth trajectory. New developments in electronic data capture (EDC) systems, wearable technology, remote monitoring tools, and artificial intelligence (AI) applications have completely changed the way trials are conducted and ushered in a period of increased accuracy, efficiency, and patient-centred care. Electronic Data Capture systems have expedited trial timelines by streamlining data collection and management procedures and lowering the errors that come with manual data entry. Concurrently, remote monitoring tools have made it easier to oversee trial operations smoothly, allowing researchers to follow protocol adherence and remotely monitor patient progress without regard to location. Wearable device integration has made it possible to monitor patients continuously, giving researchers real-time access to vital signs and health metrics and facilitating more thorough data collection and analysis. AI-driven technology is also improving patient recruitment, trial design, and data analysis; this leads to better decision-making and a deeper understanding of treatment efficacy and safety profiles. This merging of technology and healthcare improves clinical trial quality and efficiency while also creating a more welcoming and patient-focused research environment. For instance, HealthTap is a medical group and technology firm that offers telehealth virtual healthcare over the web and health applications. Their clients include US consumers, health systems, insurance companies, and self-insured businesses. HealthTap allows you to quickly connect to or arrange an appointment with a physician for a consultation by video conference, phone conversation, or text chat via the web or mobile application. (Source: https://www.healthtap.com/about/)

As a result, there is a significant surge in demand for clinical trials in the market, driven by the broad adoption of cutting-edge technologies that have the potential to completely transform medical research and healthcare delivery.

Change Towards Individualised Medicament. 

The market for clinical trials is expanding at an exponential rate, driven primarily by the shift towards personalised medicine. By providing more focused and potent therapies, personalized medicine has completely changed the healthcare industry. It is defined by treatments that are customized to each patient's unique genetics, lifestyle, and environment. To handle the complexities of personalised treatment approaches, this paradigm shift has required a corresponding evolution in clinical trial methodologies. To stratify patient populations and make sure the right treatment is given to the right patient at the right time, clinical trials are increasingly concentrating on finding biomarkers and genetic signatures. By minimizing side effects and optimizing therapeutic efficacy, this precision medicine approach not only improves t...

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...