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

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C.2 C.2 Annex: Methods for Statistical Power and Sample Size Calculations Table S6.7b: Minimum detectable odds ratios [MDOR] for gene-environment interaction (statistical power of 0.80; dominant effects model for the genetic Table S6.7b: variant), Minimum by allele detectable frequency, odds by ratios non-genetic [MDOR] risk for gene-environment factor prevalence, interaction and by significance (statistical level; power nested of 0.80; case-control dominant effects study model with 4 for controls the per genetic case. variant), by allele frequency, by non-genetic risk factor prevalence, and by significance level; nested case-control study with 4 controls per case. Minimum detectable OR for gene-environment interaction (4 controls) 10 000 cases 5000 cases 4000 cases 3000 cases 2500 cases 2000 cases 1500 cases 1000 cases 700 cases 500 cases 400 cases 300 cases 200 cases 150 cases Critical p-value Non-genetic factor prevalence Genotype prevalence 0.05 0.05 0.05 8.7 6.8 5.0 4.2 3.7 3.1 2.6 2.3 2.1 1.9 1.8 1.7 1.6 1.4 0.05 0.05 10 -4 35.5 21.4 12.6 9.3 7.6 5.8 4.5 3.6 3.1 2.8 2.6 2.3 2.1 1.7 0.1 0.05 0.05 6.1 4.9 3.8 3.2 2.9 2.5 2.2 1.9 1.8 1.7 1.6 1.5 1.5 1.3 0.1 0.05 10 -4 19.7 13.1 8.4 6.5 5.4 4.3 3.5 2.8 2.5 2.3 2.2 2.0 1.8 1.6 0.25 0.05 0.05 5.3 4.3 3.3 2.8 2.6 2.2 2.0 1.8 1.6 1.6 1.5 1.4 1.4 1.3 0.25 0.05 10 -4 17.8 11.6 7.3 5.6 4.7 3.7 3.0 2.5 2.2 2.1 1.9 1.8 1.7 1.5 0.5 0.05 0.05 9.3 6.7 4.6 3.8 3.3 2.7 2.3 2.0 1.8 1.7 1.6 1.5 1.5 1.3 0.5 0.05 10 -4 38.6 16.0 10.2 7.7 5.5 4.1 3.2 2.7 2.4 2.3 2.0 1.9 1.6 0.05 0.1 0.05 5.8 4.7 3.7 3.1 2.8 2.5 2.1 1.9 1.8 1.7 1.6 1.5 1.4 1.3 0.05 0.1 10 -4 17.2 11.9 7.8 6.1 5.1 4.1 3.4 2.8 2.4 2.3 2.1 1.9 1.8 1.5 0.1 0.1 0.05 4.3 3.6 2.9 2.5 2.3 2.1 1.8 1.7 1.6 1.5 1.4 1.4 1.3 1.2 0.1 0.1 10 -4 10.9 7.9 5.5 4.5 3.9 3.2 2.7 2.3 2.1 1.9 1.8 1.7 1.6 1.4 0.25 0.1 0.05 3.7 3.1 2.5 2.3 2.1 1.9 1.7 1.5 1.5 1.4 1.4 1.3 1.3 1.19 0.25 0.1 10 -4 10.1 7.1 4.8 3.9 3.4 2.8 2.4 2.0 1.9 1.7 1.7 1.6 1.5 1.3 0.5 0.1 0.05 6.0 4.5 3.3 2.8 2.5 2.2 1.9 1.7 1.6 1.5 1.5 1.4 1.3 1.2 0.5 0.1 10 -4 24.4 9.6 6.5 5.1 3.8 3.0 2.5 2.2 2.0 1.9 1.7 1.6 1.4 0.05 0.2 0.05 4.5 3.7 3.0 2.6 2.4 2.1 1.9 1.7 1.6 1.5 1.5 1.4 1.3 1.2 0.05 0.2 10 -4 12.0 8.6 5.9 4.7 4.1 3.3 2.8 2.3 2.1 2.0 1.9 1.7 1.6 1.4 0.1 0.2 0.05 3.4 2.9 2.5 2.2 2.0 1.8 1.7 1.5 1.4 1.4 1.3 1.3 1.3 1.18 0.1 0.2 10 -4 8.1 6.0 4.3 3.6 3.1 2.7 2.3 2.0 1.8 1.7 1.6 1.5 1.5 1.3 0.25 0.2 0.05 3.1 2.6 2.2 2.0 1.8 1.7 1.5 1.4 1.4 1.3 1.3 1.2 1.2 1.15 0.25 0.2 10 -4 8.2 5.7 3.9 3.2 2.8 2.4 2.1 1.8 1.7 1.6 1.5 1.4 1.4 1.3 0.5 0.2 0.05 5.2 3.8 2.8 2.4 2.2 1.9 1.7 1.5 1.5 1.4 1.4 1.3 1.3 1.18 0.5 0.2 10 -4 9.8 5.6 4.3 3.2 2.6 2.1 1.9 1.8 1.7 1.6 1.5 1.3 282 * assuming main effects odds ratios of 1.2 for genetic and 2.0 for non-genetic risk factor ** highlighted are the combinations where MDOR is 1.5 or smaller
C.2 Annex: Methods for Statistical Power and Sample Size Calculations C.2.4 Sample size requirements for validation/calibration sub-studies When a sub-study with replicate measurements is used for regression calibration, e.g. of blood-based exposure measurements, the accuracy of the adjustment will depend jointly on the strength of correlation (ρ ,) between the replicate measurements, the size of the m1,m2 sub-study (N), the strength of association (standardized difference A=βσ ) between exposure measurements and disease risk and the number of cases with disease used for relative risk modelling824, 825 . This can be expressed by the following equation, using the ratio of observed number of cases (D) over the effective number of cases (D) left after calibration correction as a relative efficiency criterion824 �1 � �0 A � � �� : 2 D 2 m1, m2 D A 2 D m1, m2 N For example, when the correlation ρ , between replicate measurements is at least 0.40, m1,m2 and for a standardized difference (A) in exposure level between cases and controls corresponding to an expected relative risk of 4.0 between the extreme quintiles of the exposure measurement (i.e., A=0.50), a calibration sub-study should include at least 5.25 times the number of cases with disease in a nested case-control study to limit the loss in effective number of cases to 20% (i.e., D/D = 1/0.80 = 1.25). At higher correlations between replicate measurements, or in situations where ~ relative risk estimates (association measure A) are lower, this same criterion can be met by a smaller reproducibility/calibration sub-study. For example, when the standardized difference in exposure between cases and controls corresponds to an expected relative risk [odds ratio] of 2.0 between the extreme quintiles (i.e., A=0.25), the size of the sub-study should be only about 1.3 times the number of cases, which is four times fewer than in the previous example. Table S6.8 shows the sample size numbers (N) required for different combinations of ρ , A, D. m1,m2 1 ~ � � ~ � 1� � On the basis of the estimated sample size requirements for calibration sub-studies (Table S6.8), and anticipating that most relevant exposure measurements (i.e. those that in practice can be expected to allow detection of a true relative risk of at least 1.5 or higher; see also Table S6.9) will generally have a correlation of at least 0.30 between short-term replicate measurements (e.g. over a one-year time interval), we propose a study size for validation/ calibration sub-studies of 10,000 subjects in total. Of these, 6,000 will be in the first (baseline) recruitment round, and 4,000 in the second round of data collection, five years later. 283 � af � 2 cf � af � 2af 1� C.2
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The National Cohort A prospective e
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