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Original article| Volume 49, ISSUE 8, P907-916, August 1996

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Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis

  • Guo-Wen Sun
    Correspondence
    Address reprint requests to Dr Sun's current address: Guo-Wen Sun, 718 Willowbrook Drive, Norristown, PA 19403.
    Footnotes
    Affiliations
    The Heart Institute, Good Samaritan Hospital, Los Angeles, California 90017-2395, United States
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  • Thomas L. Shook
    Footnotes
    Affiliations
    The Heart Institute, Good Samaritan Hospital, Los Angeles, California 90017-2395, United States
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  • Gregory L. Kay
    Footnotes
    Affiliations
    The Heart Institute, Good Samaritan Hospital, Los Angeles, California 90017-2395, United States
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  • Author Footnotes
    1 The first author (G. W.S.) is partially supported by the B.C. McCabe Foundation (Whittier, CA) and The Los Angeles Thoracic and Cardiovascular Foundation (Los Angeles, CA).
DOI:https://doi.org/10.1016/0895-4356(96)00025-X
Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis
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      Abstract

      The use of bivariable selection (BVS) for selecting variables to be used in multivariable analysis is inappropriate despite its common usage in medical sciences. In BVS, if the statistical p value of a risk factor in bivariable analysis is greater than an arbitrary value (often p = 0.05), then this factor will not be allowed to compete for inclusion in multivariable analysis. This type of variable selection is inappropriate because the BVS method wrongly rejects potentially important variables when the relationship between an outcome and a risk factor is confounded by any confounder and when this confounder is not properly controlled. This article uses both hypothetical and actual data to show how a nonsignificant risk factor in bivariable analysis may actually be a significant risk factor in multivariable analysis if confounding is properly controlled. Furthermore, problems resulting from the automated forward and stepwise modeling with or without the presence of confounding are also addressed. To avoid these improper procedures and deficiencies, alternatives in performing multivariable analysis, including advantages and disadvantages of the BVS method and automated stepwise modeling, are reviewed and discussed.

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      Article info

      Publication history

      Accepted: November 13, 1995

      Footnotes

      ☆The results, analyses, and opinions reported herein are those of the authors and in no way reflect on the opinions or judgments of the hospital, the B.C. McCabe Foundation, or The Los Angeles Thoracic and Cardiovascular Foundation.

      ☆☆This project would not have reached this stage without critical review from one of the anonymous referees.

      Identification

      DOI: https://doi.org/10.1016/0895-4356(96)00025-X

      Copyright

      © 1996 Published by Elsevier Inc.

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