Clinical Trial Management Statistics 2024 – Everything You Need to Know

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Are you looking to add Clinical Trial Management to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Clinical Trial Management statistics of 2024.

My team and I scanned the entire web and collected all the most useful Clinical Trial Management stats on this page. You don’t need to check any other resource on the web for any Clinical Trial Management statistics. All are here only 🙂

How much of an impact will Clinical Trial Management have on your day-to-day? or the day-to-day of your business? Should you invest in Clinical Trial Management? We will answer all your Clinical Trial Management related questions here.

Please read the page carefully and don’t miss any word. 🙂

Best Clinical Trial Management Statistics

☰ Use “CTRL+F” to quickly find statistics. There are total 69 Clinical Trial Management Statistics on this page 🙂

Clinical Trial Management Latest Statistics

  • Issuing NIAID and partner components intend to commit an estimated total of up to $35.5M, depending on funds availability, in Core funding in FY21 for 4 Statistical and Data Management Centers. [0]
  • Suppose that researchers decide that a 5% false positive error rate is acceptable. [1]
  • The 0.05 cut off is used to control the “false positive” error rate (i.e., to ensure that it is not greater than 5% when the null hypothesis is true). [1]
  • However, researchers can decide for themselves if a 5% false positive error rate is appropriate. [1]
  • If a false positive error is very costly then researchers may opt for a 1% false positive rate. [1]
  • If a false positive error is not costly, then a 10% could be used. [1]
  • The standard 5% false positive error rate is often used since this is the regulatory hurdle for approval of a new intervention. [1]
  • A 95% confidence interval can be thought of as an interval that has 95% probability of covering the parameter of interest (note that this is distinct from a value having 95% probability of falling into an interval). [1]
  • This is to say that if a trial was repeated a very large number of times and each time a 95% confidence interval estimate of the treatment effect was obtained, then 95% of the confidence intervals would cover the true treatment effect. [1]
  • This decision rule sets the false positive error rate at 5% for a single test. [1]
  • However if a researcher conducts several tests, then the probability of making at least one false positive error is greater than 5%. [1]
  • For example if a researcher plans to conduct two hypothesis tests, then the trial wise error rate could be controlled at 5% by conducting each individual test at 0.025. [1]
  • Informative and non informative prior distributionsAn informative prior distribution gives preferences to some values of the quantity of interest as being more likely than others. [2]
  • If the posterior probability that an endpoint lies in an interval is 0.95, then this interval is called a 95 percent credible interval. [2]
  • A reasonable degree of certainty could be 90%, 95% or even 99%. [2]
  • According to Bernardo and Smith , an optimal decision for choosing the true model among a finite set of possible models for a given data set is to choose the model with highest posterior probability. [2]
  • pD can be estimated as the posterior mean of the deviance minus the deviance evaluated at the posterior mean of the parameters. [2]
  • The predicted outcome for the group which received only SOC matched what was observed in the CALGB9344 trial. [3]
  • The underlying formulas used by CancerMath and similar web based programs add the risk of death conferred by disease and treatment related variables to the underlying actuarial risk of death to generate predicted outcomes.[1, 2]. [3]
  • those receiving AC, and standard errors were estimated using Greenwood’s formula[8]. [3]
  • Ninety five percent confidence intervals were reported, and significance was assessed at the 0.05 level. [3]
  • Multiplier correction factorWe found that the CancerMath prediction model underestimated mortality for the NSABP B15 participants. [3]
  • Observed survival at 15 years for NSABP B15 ageER PR subgroups ranged from 33.7% . [3]
  • Predicted survival using CancerMath ranged from 57.1% . [3]
  • 40–59 ER+/PR ) to 67.1%. [3]
  • In total, the observed 15 year survival for the 1,450 NSABP B15 participants without ER+/PR+ breast cancers was 47.7% versus the predicted survival of 61.6%, a difference of 13.9%. [3]
  • For the NSABP B15 population, the curve generated by the CancerMath program overestimated survival in the years immediately following diagnosis. [3]
  • Multiplier corrections from Table 1 were multiplied by the predicted survival in each subgroup to obtain the adjusted predicted survival for each group. [3]
  • The NSABP corrections from Table 3 were multiplied by each patient’s 15year survival estimate from CancerMath to obtain 15 year adjusted predicted survival estimates. [3]
  • The mortality fraction was then applied to obtain yearly estimates for predicted overall survival from Years 1 to 14 and random draws from the Uniformdistribution were utilized to simulate a predicted survival time for each patient. [3]
  • as estimated by the Kaplan Meier method, as well as simulated predicted annual survival rates for each arm, after applying the B15 multiplier correction and the SEER mortality fraction. [3]
  • Looking closer, at year 5, predicted and observed survival with AC + P were 65.8% and 71.4% , respectively; and at years 10 and 15 were 52.9% and 61.8% , and 45.5% and 54.8% , respectively. [3]
  • We found that the CancerMath prediction model underestimated mortality for the NSABP B15 participants. [3]
  • Returning to the training set, this new mortality fraction was used to allocate a percentage of the predicted 15 year total mortality burden to each preceding year. [3]
  • Again looking closer at our ER/PRsubgroup, 15 year survival within the predicted virtual group treated with AC aged 20–39 compared to the observed group treated with AC was 49.5% versus 49.7%, and 45.6% versus 45.4% in the group aged 40–59 years. [3]
  • Finally, overall observed survival for the AC + P and AC groups was estimated using the Kaplan Meier method at yearly intervals and is shown in Tables 4 and 5. [3]
  • Observed and predicted yearly survival were compared, as shown in Fig 1. [3]
  • Predicted vs observed overall survival of CALGB patients receiving either AC+P or AC. [3]
  • In contrast, we failed to reject the null hypothesis that yearly observed estimates and predicted values for AC were significantly different. [3]
  • 70.6% of all Clinical Trial Managers are women, while29.4%aremen. [4]
  • The most common ethnicity of Clinical Trial Managers is White (73.0%), followed by Asian (12.1%) and Hispanic or Latino (9.7%). [4]
  • In 2021, women earned 89% of what men earned. [4]
  • The top 10% of highest paid Clinical Trial Managers earn as much as $151,000 or more. [4]
  • Comparatively, there are 12.1% of the Asian ethnicity and 9.7% of the Hispanic or Latino ethnicity. [4]
  • White, 73.0% Asian, 12.1% Hispanic or Latino, 9.7% Black or African American, 3.3% Unknown, 1.3% American Indian and Alaska Native, 0.6%. [4]
  • Using the Census Bureau data, we found out how the percentage of each ethnic category trended between 2010 2019 among Clinical Trial Managers. [4]
  • Interestingly enough, the average age of Clinical Trial Managers is 40+ years old, which represents 63% of the population. [4]
  • 60% of Clinical Trial Managers earn that degree. [4]
  • A close second is Master’s Degree with 22% and rounding it off. [4]
  • Bachelors, 60% Masters, 22% Doctorate, 7% Associate, 5%. [4]
  • Other Degrees, 6% Clinical Trial Manager Wage Gap By Education Clinical Trial Manager Employment Statistics. [4]
  • By looking over 971 Clinical Trial Managers resumes, we figured out that the average Clinical Trial Manager enjoys staying at their job for 1 2 years for a percentage of 39%. [4]
  • Clinical Trial Manager 18.45% Keno Manager 14.43% Shelter Director 12.85% Geriatric Care Manager 10.10% Environmental Services Director 8.41% Design Engineering Manager 8.13% Owner/Project Manager 2.16% Profession. [4]
  • The most common foreign language among Clinical Trial Managers is Spanish at 48.5%. [4]
  • The secondmost popular foreign language spoken is German at 9.1% and French is the third most popular at 9.1%. [4]
  • Spanish, 48.5% German, 9.1% French, 9.1% Chinese, 6.1% Arabic, 6.1% Other, 21.1%. [4]
  • With the planned sample size, we have 80% power to detect a 15% improvement in good neurological outcomes at a two sided statistical significance level of 5%. [5]
  • In the US, the elderly constitute 14% of the population, while they consume over one. [6]
  • The council established the Therapeutic Trials Committee to advise and assist in the arrangement of properly controlled clinical trials on new products that seem likely on experimental grounds to have value in the treatment of disease.[15]. [6]
  • In the case of cancer patients, fewer than 5% of adults with cancer will participate in drug trials. [6]
  • According to the Pharmaceutical Research and Manufacturers of America , about 400 cancer medicines were being tested in clinical trials in 2005. [6]
  • According to one study, approximately 75% of articles retracted for misconduct related reasons have no declared industry financial support. [6]
  • The editorial noted that contract research organizations had, by 2000, received 60% of the grants from pharmaceutical companies in the US. [6]
  • An estimated 40% of all clinical trials now take place in Asia, Eastern Europe, and Central and South America. [6]
  • In the US, sponsors may receive a 50 percent tax credit for clinical trials conducted on drugs being developed for the treatment of orphan diseases. [6]
  • The New York TimesOf the drugs started in clinical trials on humans, only 10 percent secure F.D.A. approval. [6]
  • Requirements differ according to the trial needs, but typically volunteers would be screened in a medical laboratory for[97]. [6]
  • The New York TimesOf the drugs started in clinical trials on humans, only 10 percent secure F.D.A. approval. … [6]
  • ReputationOver 95% of clients are fromsatisfied referrals. [7]

I know you want to use Clinical Trial Management Software, thus we made this list of best Clinical Trial Management Software. We also wrote about how to learn Clinical Trial Management Software and how to install Clinical Trial Management Software. Recently we wrote how to uninstall Clinical Trial Management Software for newbie users. Don’t forgot to check latest Clinical Trial Management statistics of 2024.


  1. nih –
  2. nih –
  3. fda –
  4. plos –
  5. zippia –
  6. nih –
  7. wikipedia –
  8. mcdougallscientific –

How Useful is Clinical Trial Management

One of the primary benefits of clinical trial management is the structured and standardized approach it provides. A well-organized trial management process ensures that each trial follows a specified protocol, minimizing the risk of errors or bias. This allows for the fair evaluation of new drugs, medical devices, or treatment approaches. Such impartial scrutiny is essential for medical progress, enabling researchers and regulators to make informed decisions about the effectiveness and safety of medical interventions.

Furthermore, clinical trial management ensures patient safety and ethical standards are upheld throughout the process. Regulatory bodies enforce stringent guidelines, such as obtaining informed consent from participants, protecting their rights, and continuous monitoring for adverse events. The management team oversees compliance with these regulations, maintaining the integrity of the trial while safeguarding patient welfare. By adhering to rigorous ethical principles, clinical trial management builds trust and helps the public feel confident in the medical advancements they stand to benefit from.

One big advantage of efficient clinical trial management is the potential for reducing the time it takes for new treatments to reach patients. Streamlining trial processes and removing unnecessary bureaucracy can expedite the discovery and approval of effective treatments. Medical conditions often have limited treatment options, and every delay in the clinical trial stage can mean prolonged suffering or even lost lives. Hence, effective management of trials is vital for accelerating the availability of new and improved therapies for patients in dire need.

Additionally, clinical trial management promotes collaboration and communication among various stakeholders. These trials involve a multidisciplinary team comprising researchers, healthcare professionals, ethical review boards, regulators, and technology companies, just to name a few. Effective management fosters coordination between these diverse groups, allowing for efficient recruitment of participants, data collection, and analysis. Effective trials rely on meaningful collaboration, driving innovation and ensuring that research is translated into practice effectively.

Lastly, clinical trial management provides the foundation for critical evaluation and continuous improvement within the field of medicine. Rigorous analysis of trial data not only assesses the safety and efficacy of interventions but also helps identify factors that may contribute to treatment success or failure. The insights gained from this evaluation process play an essential role in shaping future research and clinical practice. By learning from previous trials, researchers can refine their hypotheses, study designs, and selection criteria, thus optimizing the utility and efficiency of future clinical trials.

In conclusion, clinical trial management is incredibly useful for achieving scientific advancements and delivering safer and more effective treatments to patients. Through ensuring a structured approach, upholding ethical standards, expediting time to market, promoting collaboration, and fostering improvement, this field drives the progress that impacts countless lives. By continuously improving trial management processes, we can pave the way for medical breakthroughs that improve the health and well-being of individuals globally.

In Conclusion

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