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

A.6 Planned statistical analyses and statistical power considerations
Within these approaches different methods of confounder adjustment (general fixed and
random mixed models) will be used, for example, for different association patterns of the
risk factors and for risk factor–outcome relationships in cases of clustered risk factors. This
is essential for clustered exposures such as environmental risk factors or even nutritional
exposures, which may be clustered for substantial parts of the entire cohort.
Some general examples of these methods are outlined in Table 6.1.
A.6.2.2 Approaches to multicenter analysis
To account for the multicenter study design, multilevel statistical models can be used that
integrate information on exposure-disease relationships on two complementary levels of
observation, namely: (a) within-center relationships, which reflect the relationships at the
individual level in each of the centers; and (b) and a between-center relationship, which
captures the association that may exist between exposure and disease risk at the aggregate
level. Such multilevel models can take confounding variables fully into account, can
be extended to simultaneously correct for bias in estimated exposure–disease relationships
that may result from measurement errors, using data from calibration substudies, and are
also well-suited for studying or identifying specific area effects on health 784-789 .
A.6.2.3 Special statistical methods
This section describes some of the statistical analysis methods that go beyond the standard
statistical methods commonly used in cohort studies which were described in the previous
Sect. A.6.2.1. These special methods will play a major role in the analysis in the National
Cohort for two main reasons:
� The specific design of the study (e.g., repeated measurements for a subsample of all
cohort members within less than 1 year) requires special statistical methods.
� This study will have a large public health impact. Concepts such as attributable risk or
disability adjusted life-years, therefore, deserve wider attention.
A.6.2.3.1 Replicate risk factor assessments, and measurement errors
General remarks on measurement error for exposure assessment
In previous sections, the effects of random measurement errors and misclassification by
exposure level were not discussed. In practice, however, measurements of risk factors or
exposures generally contain a considerable degree of error. If no corrections are made for
these effects, estimates of RR corresponding to measured exposure levels generally will
not reflect RR levels with respect to the true underlying exposure variable 790-794 .
In unifactor analyses, assuming there are no confounding effects related to other covariates,
random measurement errors in a continuous and quantitatively measured exposure
variable will underestimate RRs. This statistical phenomenon is often referred to as “attenuation
bias” or “regression dilution” bias 795 . Likewise, when a quantitative exposure variable
measured with random error is dichotomized or otherwise categorized, random misclassification
by exposure categories will occur, and in unifactor, nonconfounded situations RRs
by exposure category will also tend to be underestimated. In multifactorial analyses, by
contrast, in which confounding relationships between the multiple risk factors can be found,
RRs with respect to a defined exposure measurement may be either over- or underestimated
when each of the risk factors is measured with error 796 . In most epidemiologic studies
so far, including most existing prospective cohorts, this intertwined problem has presented
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