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Academia and the Profession |

Spectrum Bias or Spectrum Effect? Subgroup Variation in Diagnostic Test Evaluation

Stephanie A. Mulherin, MSPH; and William C. Miller, MD, PhD, MPH
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From University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.


Acknowledgments: The authors thank David Ransohoff, MD; Michael Pignone, MD, MPH; and Jay Kaufman, PhD, for their thoughtful comments.

Grant Support: By the University of North Carolina STD Clinical Research Center (National Institute of Allergy and Infectious Diseases grant UO131496) and the Clinical Associate Physician Program of the General Clinical Research Center (RR00046), Division of Research Resources, National Institutes of Health.

Requests for Single Reprints: William C. Miller, MD, PhD, MPH, University of North Carolina at Chapel Hill, Department of Epidemiology, CB# 7435, 2105F McGavran-Greenberg, Chapel Hill, NC 27599-7435.

Current Author Addresses: Ms. Mulherin and Dr. Miller: University of North Carolina at Chapel Hill, Department of Epidemiology, CB #7435 McGavran-Greenberg, Chapel Hill, NC 27599-7435.


Ann Intern Med. 2002;137(7):598-602. doi:10.7326/0003-4819-137-7-200210010-00011
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Diagnostic tests must be evaluated in a clinically relevant population. However, test performance often varies across population subgroups. Spectrum bias, a term commonly used to describe this heterogeneity, is typically thought to occur when diagnostic test performance varies across patient subgroups and a study of that test's performance does not adequately represent all subgroups. Yet subgroup variation is not a bias if appropriate analyses are conducted. Failure to recognize and address heterogeneity will lead to estimates of test performance that are not generalizable to the relevant clinical populations. Heterogeneity can be addressed with relatively simple stratification procedures, limited primarily by the sample size and the precision of the estimates. This paper proposes the use of the term spectrum effect, rather than spectrum bias, and outlines strategies for using stratified sensitivity and specificity estimates, likelihood ratios, and receiver-operating characteristic curves. Investigators of diagnostic tests should consider the potential for spectrum effect seriously and should address heterogeneity in their analyses. Furthermore, clinicians should consider study samples carefully to determine whether results are generalizable to their specific patient population.

Figures

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Figure.
Receiver-operating characteristic curve for performance of an enzyme immunoassay forChlamydia trachomatis, stratified by age.P

The discriminatory accuracy for enzyme immunoassay is better for participants 24 years of age or younger (area under the curve [±SE], 0.927 ± 0.009) than for those older than 24 years of age (area under the curve [±SE], 0.819 ± 0.033). The areas under the stratified curves were significantly different ( ≤ 0.001).

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