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Academia and the Profession |

Computer-Assisted Design of Studies Using Routine Clinical Data: Analyzing the Association of Prednisone and Cholesterol

ROBERT L. BLUM, M.D., Ph.D.
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The RX Study Module is in the public domain. Because it is a research prototype, capability in INTERLISP programming is required to install it at other laboratories. Interested researchers with the requisite computing facilities to maintain the system independently may contact the authors.

Grant support: In part by grant LM-04334 from the National Library of Medicine and grant IST-8317858 from the National Science Foundation. Previous funding included grant HS-04389 from the National Center for Health Services Research, grant LM-03370 from the National Library of Medicine, and by the Pharmaceutical Manufacturers Association Foundation. The ARAMIS database is sponsored by grants AM-21393 and HS-03802 from the National Institutes of Health; and SUMEX-AIM by grant RR-00785 from the Biotechnology Resources Program, National Institutes of Health.

▸Requests for reprints should be addressed to Robert L. Blum, M.D., Ph.D.; Department of Computer Science, Margaret Jacks Hall, Stanford University, Stanford, CA 94305.


Stanford, California


© 1986 American College of PhysiciansAmerican College of Physicians


Ann Intern Med. 1986;104(6):858-868. doi:10.7326/0003-4819-104-6-858
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To facilitate the analysis of routine, longitudinal, clinical data, we developed a computer program called the RX Study Module. Our prototype uses a small online knowledge base of medicine and biostatistics to help create and execute a detailed statistical study design. The program identifies possible confounding variables, selects methods for controlling them, creates a statistical model, determines patient eligibility criteria, and retrieves data from records. We used the program to examine the hypothesis that daily prednisone administration elevates serum cholesterol. Data from 49 patients with chronic rheumatologic disorders were analyzed from a database of 1787 patients. A regression model was fitted to each patient's record. Changes in cholesterol were significantly correlated (p = 10-5) with changes in prednisone after a lag of at least 1 week and after recorded confounders were controlled: ▵cholesterol = 18. 4 loge(prednisone). Routinely collected patient data may become an important resource for generating and studying new medical hypotheses.

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