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Setting Criterion Thresholds for Estimating Prevalence: What is Being Validated?

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Abstract

Much of the debate over how best to estimate the prevalence of problem gambling in the general population is driven by a number of misconceptions, misinterpretations, and questionable, sometimes erroneous assumptions. Among the latter is the failure to understand that what is being validated is not the test but the interpretation of test scores for a specific purpose. In addition there has been a lack of attention to defining the clinical and/or epidemiologic relevance of case definitions in terms of severity and other clinical attributes, a misunderstanding of how test values are interpreted when criterion thresholds or cut-off points are selected, and a failure to replicate the validation of criterion thresholds for defining cases of problem gambling. It is argued further that the distinction between dichotomy and continuum is a false choice, and any emphasis on overestimation is misdirected. Alternative methods for evaluating tests and estimating prevalence are described and a pragmatic empirical approach to the interpretation of prevalence estimates is recommended.

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Notes

  1. The occurrence of errors in gold standards may be due to a number of sources, for example, an alternative condition mimics the biomarker changes of the condition of interest, a laboratory test may be mislabeled, a tumor is missed by MRI because it is too small, an image may be misinterpreted, or a biopsy occurs outside of the affected tumor (Reitsma et al. 2009).

  2. Since it takes an interval of time to survey all respondents, point prevalence estimates are as a practical and technical matter also period prevalence rates. As a rule of thumb, one-month rates are considered point prevalence estimates.

  3. The use of the term normal is specific to gambling and does not rule out the presence of other conditions.

  4. The term reference or criterion standard rather than gold standard is now preferred since gold standard connotes an errorless process whereas in practice this is rarely if ever true. Further, for most disorders there is no gold standard and alternative standards that are error-prone must be utilized. The study reference standard is an operational definition of diagnostic truth for evaluating the new test. Contrary to popular belief the application of a gold standard does not imply the standard is error free; in medicine, for example, some gold standards have error rates as high as 5 % or greater (Valenstein 1990).

  5. Bias in prevalence estimation refers to whether standard estimates for P given by AP tend, on average, to overestimate (positive bias) or underestimate (negative bias) the true population prevalence (Gambino 1997). If the test is imperfect then AP is a biased estimator for P; that is, in repeated studies the test will tend to overestimate (underestimate) the true P more often than AP will equal or underestimate (overestimate) P. Even biased tests will sometimes provide unbiased results, Table 2, panel B.

  6. It may be noted that whereas in Table 1 test diagnoses are represented as rows and the disorder or condition is represented as columns, some experts on test evaluation argue that columns also represent diagnoses as made by the reference standard (Kraemer 1992). Other experts disagree with this view (Pepe 2003) and argue that this may confuse readers. In the present paper, rows are identified as test diagnoses and columns are identified with the presence or absence of the target disorder.

  7. Canadian Problem Gambling Index, Ferris and Wynne (2001).

  8. Since any column (row) in a 2 × 2 table must sum to 100 %, it follows directly that knowledge of TPR (sensitivity) defines its complement FNR (false negative rate) = 100 % − TPR, and knowledge of FPR defines its complement TNR (true negative rate or specificity) = 100 % − FPR. It is important to recognize the value of these two relations, if TPR and FPR are known or estimable then the test evaluator has all the information about test properties based solely on positive test results.

  9. The counts of normal and problem gamblers were reconstructed from Boudreau and Poulin (2006, Table 1, p. 303) re-validation of SOGS-RA (SOGS Revised for Adolescents, Winters et al. 1993)), and are presented for illustrative purposes only. It is assumed in the illustration that the reference standard, self perceived problem gambler is errorless.

  10. Mathematically TPR is not considered a rate and statisticians prefer the more precise term fraction or proportion instead; the reader may consider TPR = TPF and FPR = FPF equivalent in meaning and measurement. The reader will find the terms, rate or fraction, will both appear in the literature on ROC.

  11. There is some debate over whether face validity is equivalent to content validity with the two terms often used as interchangeable. Anastasi (1988) has framed the discussion as: “Content validity should not be confused with face validity. The latter is not validity in the technical sense; it refers, not to what the test actually measures, but to what it appears superficially to measure” (p. 144). Content validity is relatively easy to measure when the construct is ability or skills that are specific to task elements but is more difficult to establish for less specific constructs such as problem gambling.

  12. In practice any theoretical position that continues to be supported over time will become accepted as established with the often unstated caveat that if new evidence cannot be explained then the theory will be revised, discarded, or replaced. Good theories survive most attacks on the validity of the theory; poor theories are unlikely to survive. Although no doubt there are instances of the latter.

  13. South Oaks Gambling Screen (Lesieur & Blume 1987).

  14. Victorian Gambling Screen, Ben Tovin et al. (2001).

  15. National Opinion [Research Center] Diagnostic Screen (Gerstein et al. 1999).

  16. PPV is obtained by applying Bayes Theorem as pre-test odds (P/(1 − P)) × test odds (LR+) = post-test odds and converting post-test odds to PPV by applying odds/(1 + odds).

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    Blase Gambino

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Correspondence to Blase Gambino.

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Gambino, B. Setting Criterion Thresholds for Estimating Prevalence: What is Being Validated?. J Gambl Stud 30, 577–607 (2014). https://doi.org/10.1007/s10899-013-9380-y

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