Biostatistical Resources in an Academic Medical Center

Details

• Personal Author:
  Cheng M ; Thatcher A ; Thiese MS
• Description:
  Biomedical research published in peer-reviewed medical literature is necessary for promoting understanding, addressing novel clinical problems, and educating professionals on new surgical techniques and approaches. In spite of this need, all forms of biomedical research, and surgical research in particular, requires addressing increasingly complex methodological trials and meta-analyses compared to traditional clinical studies conducted 20 years ago. Biostatistics is a tool which clinicians and researchers can rely upon to analyze associations and relationships within the data. The exact nature of analysis employed, and resulting data findings and conclusions are governed by several factors, which can be found in Table 1. Biostatistics are consequently applied to calculate mathematical relationships and trends in data. Peer-reviewed, published results is the primary way clinicians and researchers advance their practice, which is increasingly needed due to the given rate of change in the standard of care and the growing complexity in medical care such as innovative treatments, surgical approaches, guidelines, and patient involvement. The number of clinical peer-reviewed publications, particularly surgery specific randomized trials, have noticeably increased over the last 12 years. Similarly, there has been a meaningful increase in the sophistication and complexity of the statistical analyses of these articles. Most articles, including many of the seminal articles published in the 1970s and 1980s relied upon t-tests and descriptive statistics (i.e., means, standard deviations, range, etc.) as the only statistical tests for the publication. These same statistical methods would likely not pass the current peer-review process. Analytical approaches have grown in both complexity and thoroughness in the past 12 years, with a variety of novel statistical tests, sub-analyses and post-hoc methods that are used to interpret, understand and analyze the increasingly rich and complex data collected with modern clinical trials and non-clinical trials such as meta-analysis and large data analysis. Most meaningfully, while there are more robust analytical methods available for evaluation of biomedical research data, elementary statistical tests, such as the t-test, remain the main or only statistical test in surgical research. Another report supports the concern that studies are using, and often misusing, basic parametric statistical tests more frequently even though statistical analytical methods have become more complex and robust in recent years. This frequently results in the application of incorrect test(s) being used to evaluate data, and potentially arriving at incorrect conclusions. Incorrect conclusions can impair the acceptance of novel medical treatments and potentially harm patients. Several recent reviews of published peer-reviewed studies determined almost half of current peer-reviewed clinical research articles have as a minimum one statistical error, many of which result in potential misinterpretation of results and incorrect conclusions. A review systematically evaluated biostatistical analyses of 100 orthopedic surgery peer-reviewed publications. This review concluded that 17% of the publications' conclusions were not warranted based on the analytical results, and another analytical method should have been used in 39% of the papers. Another review of peer-reviewed surgical specific clinical research found that 71 out of 91 analytical papers (78%) had errors in the usage of essential statistical analyses. The peer-reviewed manuscripts frequently failed to appropriate test for statistical significance, provided probability values (P values) without referencing a statistical test, and inappropriately applied statistical techniques. Traditionally, these issues were addressed solely by the clinical researcher, who had relatively limited biostatistical analytical skills. However, these issues are most easily addressed by clinical researchers utilizing the many biostatistical resources available at most academic medical centers. Well-designed biostatistical resources for clinical research unburden the clinical researcher from having to analyze all of the data they collect, but instead provide an array of resources from which the researcher can use to appropriately and adequately analyze their valuable data. These resources include utilization of clinical and translational sciences (CTS) centers, more widespread and early involvement of biostatisticians in study design as well as the evaluation of data, and continuing education for new statistical methods. This paper aims to describe these resources in more detail. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Analytical Processes Biostatistics Data Processing Medical Research Statistical Analysis
• ISSN:
  2072-1439
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 8 ; Utah
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  10
• Issue:
  7
• NIOSHTIC Number:
  nn:20067281
• Citation:
  J Thorac Dis 2018 Jul; 10(7):4678-4681
• Contact Point Address:
  Matthew S. Thiese, PhD, MSPH. Rocky Mountain Center for Occupational & Environment Health, Department of Family and Preventive Medicine, School of Medicine, University of Utah, 391 Chipeta Way, Suite C, Salt Lake City, UT 84108, USA
• Email:
  matt.thiese@hsc.utah.edu
• Federal Fiscal Year:
  2018
• Performing Organization:
  University of Utah
• Peer Reviewed:
  False
• Start Date:
  20050701
• Source Full Name:
  Journal of Thoracic Disease
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7ea02cfe4e20d97431755fc887e4c19eb27d00385279e12e42beb8c46fe90440daed85834fdce0962c3d0d2a42183d33a6dcb9049c5e3040152a12b8b3cba1ab
• Download URL:
  https://stacks.cdc.gov/view/cdc/231387/cdc_231387_DS1.pdf
• File Type:
  [PDF - 120.50 KB ]