Journal of Medical Systems

, Volume 8, Issue 3, pp 205–212 | Cite as

Development of a friendly, self-teaching, interactive statistical package for analysis of clinical research data

The bright stat-pack
  • D. Rodbard
  • B. R. Cole
  • P. J. Munson


We have developed a new statistical analysis package for use by the clinical investigator, the clinician, and the laboratory researcher. This package attempts to implement the following philosophy: (1) The programs should be essentially self-teachaing, friendly, and forgiving; (2) the grograms should educate the user regarding the underlying theory, assumptions, and interpretation of the statistical methods involved; (3) the programs should automatically test relevant assumptions and warn the user when these assumptions appear to have been violated; (4) the programs should make recommendations about the availability of alternative statistical methods and automatically perform such analyses when indicated; (5) the programs should interpret the results; and (6) the programs should mimic, insofar as possible, the logic used in a routine, elementary statistical consultation. Several programs have been developed, extensively tested, and used.


Statistical Method Clinical Research Analysis Package Research Data Statistical Analysis Package 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Goldberg, R. N.,BRIGHT User's Guide (Version 3.04, January 1979) (Report CBM-TR-95), Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick.Google Scholar
  2. 2.
    Snedecor, G. W., and Cochran, W. G.,Statistical Methods, seventh ed., Iowa State University Press, Ames, 1980.Google Scholar
  3. 3.
    Conover, W. J., and Iman, R. L., Rank transformations as a bridge between parametric and nonparametric statistics.Am. Statistician 35: 124–133, 1981.Google Scholar
  4. 4.
    Bennett, C. A., and Franklin, N. L.,Statistical Analysis in Chemistry and the Chemical Industry. Wiley, New York, 1954, pp. 663–688.Google Scholar
  5. 5.
    Rodbard, D., Lenox, R. H., Wray, H. L., and Ramseth, D., Statistical characterization of the random errors in the radioimmunoassay dose response variable.Clin. Chem. 22:350–358, 1976.Google Scholar
  6. 6.
    Brownlee, K. A.,Statistical Theory and Methodology in Science and Engineering, Wiley, New York, 1960, pp. 272–369.Google Scholar
  7. 7.
    Ozols, R. F., Corden, B. J., Jacobs, J., Ostchega, Y., and Young, R. C., High dose cisplatin in hypertonic saline.Ann. Intern. Med. Submitted for publication.Google Scholar
  8. 8.
    Knott, G., MLAB—An On-line Modeling Laboratory, Beginners Guide, Division of Computer Research and Technology, National Institutes of Health, Bethesda, Maryland, May 1979.Google Scholar

Copyright information

© Plenum Publishing Corporation 1984

Authors and Affiliations

  • D. Rodbard
    • 1
    • 2
  • B. R. Cole
    • 1
    • 2
  • P. J. Munson
    • 1
    • 2
  1. 1.National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesda
  2. 2.Division of Computer Research and TechnologyNational Institutes of HealthBethesda

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