Scientific Concept of the National Cohort (status ... - Nationale Kohorte

A.6 Planned statistical analyses and statistical power considerations
A.6.4.5 Statistical power for studies of sensitivity and specificity of diagnostic
markers for early detection
The National Cohort’s biorepository of blood (serum, plasma, and intact white blood cells)
and urine samples will provide a valuable resource for validating blood-based, candidate
biomarkers for the early detection of specific forms of chronic disease. Due to the prospective
study design, it is possible to examine whether a given biomarker (or combination of
markers) can predict disease occurrence ahead of its “natural” date at diagnosis (estimation
of “lead time"). Furthermore, the prospective study design helps avoid biases in the estimation
of sensitivity and specificity of diagnostic markers that can occur when such estimations
are based on comparisons between cases with advanced (clinically detectable) disease
and control subjects (for example, identification of nonspecific markers that reflect a general
inflammatory response to a tumor). In addition to blood- or urine-based biomarkers, imaging
methods (e.g., measures of brain structures) for early diagnosis and prediction of clinical
disease occurrence (e.g., specific forms of dementia) can also be prospectively evaluated
in the National Cohort.
In most situations, we anticipate that markers for early detection will be measured on a continuous
scale. Concerning sample size calculation, to guarantee an appropriate amount of
specificity, the minimally acceptable false-positive rate [FPR ] must first be fixed. Then we
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test whether the marker provides a minimally acceptable level (TPR ) of true positive detec-
0
tion rates (sensitivity). Sample size requirements can then be calculated corresponding to
tests with sufficient statistical power to detect disease with TPR0, assuming alternative true
817 positive rates TPR . 1
For relatively rare forms of disease, very high rates of specificity are needed (compared
to the number of true-positive cases) to avoid a massive excess of invasive diagnostic
work-up in individuals who are in reality free of the disease. Depending on the prevalence
of disease, this generally implies specificity levels of 99% or higher. However, the specificity
criterion can be relaxed if the marker is to be used for a first screening, to be followed
by a second, still relatively noninvasive screening tool with which specificity can be further
increased. For example, in the case of ovarian cancer, blood-based markers could be
used as a first screening, followed by transvaginal echography as a second-level screening
tool818 . In this type of situation, a novel screening marker may be useful, for example, when
it can detect disease up to 2 years in advance of its natural diagnosis, with a specificity of
98% and TPR of 20%.
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Table 6.7 shows the numbers of cases with disease required for the statistical evaluation
of a candidate diagnostic test that has a specificity of 0.95, 0.98, or 0.995 and achieving
80% statistical power in a one-sided statistical test with a significance level of 0.05, testing
the null hypothesis that sensitivity is less than TPR 0 , against the alternative hypothesis that
sensitivity is at least TPR 0 . The table indicates, for example, that for a diagnostic continuous
marker test for which a specificity threshold is set at 0.98, a statistical test that the
diagnostic tool has at least 20% sensitivity, when TPR 1 is at last 0.40, will require a nested
case-control study of at least 57 cases of disease and 570 control subjects for comparison.
Although a targeted level of specificity (FPR 0 ) must be fixed, the calculation method used
for Table 6.7 also includes an estimation step for specificity, using a prespecified tolerance
(constant ε) for the degree of accuracy of this estimate. In our calculations, the tolerance
ε was fixed at 0.005. As reflected by the data in Table 6.7, sample size requirements generally
decrease with improving sensitivity – that is, the higher the FPR is expected to be,
the higher the number of cases required to establish a true-positive rate (TPR) at TPR 0 .
Furthermore, sample size requirements increase when the underlying TPR 1 is closer to the
estimated TPR 0 . With regard to the FPR, which should be at a level of maximally FPR 0 (plus
the tolerance constant ε) particularly the number of control subjects must be large enough
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A.6