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

A.6 Planned statistical analyses and statistical power considerations
YLD = # of incident cases in that period × average duration of the disease × disability weight.
The disability weight reflects the severity of the disease on a scale from 0 (perfect health)
to 1 (death) 800 .
The DALY concept has been used relatively little in epidemiologic research in Germany. Terschüren
et al. (2009) 801 estimated the future burden of disease using the population forecast
with respect to to age and sex distribution only. In the National Cohort, long-term observations
can be used to assess the effect of changing risk factor patterns on population health.
A.6.2.4 risk prediction models
In risk prediction modeling, a first step will be to develop a discriminative RR model that
includes the relevant predictor variables. For an analysis within the full cohort, ”time to disease
onset” can be analyzed by means of a Cox-proportional-hazards regression. In a nested
case-control setting with a (bivariate) disease outcome, a risk prediction model can be
derived by means of a logistic regression model. Within such models new markers can be
combined with established risk information to assess their significance and their relevance
with respect to risk prediction. The general goal of risk prediction models will be to integrate
the information from a potentially large series of risk factors; thus, model selection procedures
such as stepwise-model selection or boosting need to be applied 802 . The choice of a
model selection method will depend on the specific situation regarding, for example, data
quality and existence of prior, additional evidence. A relevant feature in a model-building
process is ”overfitting” as the effect of a statistical model to grasp aspects that are unique
to the study sample. By incorporating cross-validation or bootstrapping methods into the
model selection process we can keep this effect under control 803 .
An important criterion for developing a risk prediction model is its ”discriminative quality.”
The discriminative quality of a risk prediction model can be measured in terms of the ”area
under the receiver operator curve” (AUROC), which summarizes the sensitivity of a risk
prediction model in relation to one-minus-specificity across all possible cut-off points. For
comparison of different risk models (e.g., a conventional model and an extended model including
new markers), the difference in the models’ AUROC values can be tested 804 . In the
presence of established risk limits the ”net reclassification gain” (NRI) can be calculated as
an alternative measure to compare classification resulting from different risk models. As an
extension to this idea of capture reclassification, the ”integrated discrimination index” (IDI)
summarizes the general improvement in discrimination. These alternative statistics provide
valuable additional insight into the discriminative quality of risk models 805 .
Finally, a further important step in constructing risk prediction models involves translating
RRs into predicted absolute risk levels. Absolute risk thresholds may be interpreted more
directly in terms of potential risks and benefits if the model is used to identify subjects for
interventions or treatments. With a cohort design absolute risk levels can be estimated
within the study 806 . External data from registries can be included as an additional source.
The two sources can be used to validate or optimize calibration of a discriminative model 807 .
In addition to case numbers, the cohort can provide valuable information on the distribution
of some, if not all, risk factors. To which extent the cohort is representative of the general
population may have to be critically evaluated at this point.
A.6.2.5 Statistical analyses for studies embedded into the National Cohort
In situations in which data from the full cohort are not available due to financial, organizational,
or other constraints, “hybrid” designs will be used. These can be properly defined
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