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

2000
1000
0
0.05 0.1 0.15A.6 Planned 0.2 statistical 0.25 analyses 0.3 and 0.35 statistical 0.4power considerations
0.45 0.5
Allele frequency
figure 6.5: Study size (number of incident cases of disease) required to detect minimal odds
RGE 1.2 1.3 1.5 1.7 2 2.5
ratios [MDOR] of various magnitudes, for gene–environment interaction with power 0.80, at significance
level *assuming of 0.05; main nested effects case-control odds ratios of 1.2 study for genetic with 4 and controls 2.0 for per non-genetic case*, assuming risk factor main effects
odds ratios of 1.2 for the alpha=0.05, genetic and prevalence 2.0 for of the non-genetic nongenetic risk factor risk 20% factor, plus a prevalence of the
nongenetic risk factor of 0.20
N cases
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
Allele frequency
RGE 1.2 1.3 1.5 1.7 2 2.5
*assuming main effects odds ratios of 1.2 for genetic and 2.0 for non-genetic risk factor
alpha=0.05, prevalence of non-genetic risk factor 20%
A.6.4.4 Effects of random measurement errors and use of repeat
measurements
The degree of correlation between exposures in the two time periods, ρ m1,m2 , is an important
determinant of the power of possible tests of “history-adjusted” exposure associations
with disease risk. For example, when correlation ρ m1,m2 is as high as 0.80, a test whether
risk is related to exposure in the second index period, adjusted for exposure at the earlier
period (i.e., testing the null hypothesis that β 2 = 0, conditionally on exposure level x 1 ) will
have a high VIF (VIF = 2.8), and a corresponding reduction in statistical power. Likewise,
a comparatively high correlation ρ m1,m2 will also limit possible power gains that could be
achieved by basing the test of exposure–disease association on a weighted average of
the exposures (i.e., testing the null hypothesis that β L = 0). For exposures (e.g., blood- or
urine-based markers) that have a correlation of ρ m1,m2 = 0.30–0.70 between replicate measurements
taken over time, a theoretical (maximal) average gain in effective sample size of
about 40–60% can be calculated, under the assumption that random errors of the replicate
measurements are not correlated and that disease risk is equally associated with the exposure
measurements at either time point (see Annex C.2.4).
figure 6.6 illustrates both relative and absolute gains in statistical power that can be obtained
by (equally) averaging exposures from two time periods. This specific example focuses
on kidney cancer, showing how statistical power will increase with increasing numbers
of incident cases until the year 2032. The assumed strength of association is that of
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A.6