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. 2016 Jan:69:245-7.
doi: 10.1016/j.jclinepi.2015.04.005. Epub 2015 Apr 18.

Prediction models need appropriate internal, internal-external, and external validation

Ewout W Steyerberg  1 , Frank E Harrell Jr  2
Affiliations

Prediction models need appropriate internal, internal-external, and external validation

Ewout W Steyerberg et al. J Clin Epidemiol. 2016 Jan.
No abstract available

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Conflict of interest statement

There are no conflicts of interests for both authors.

Figures

Figure 1
Figure 1
Schematic representation of apparent, split-sample, and bootstrap validation. Suppose we have a development sample of 1,000 subjects (numbered 1,2,3,..1000). Apparent validation assesses performance of a model estimated in these 1000 subjects on the sample. Split-sample validation may consider 50% for model development, and 50% for validation. Bootstrapping involves sampling with replacement (e.g., subject number 1 is drawn twice, number 2 is out, etcetera), with validation of the model developed in the bootstrap sample (Sample*) in the original sample.
Figure 2
Figure 2
Schematic representation of internal-external cross-validation and external validation. Suppose we have 4 centers (a – d) in our development sample. We may leave 1 center out at a time to cross-validate a model developed in the other centers. One such validation is illustrated: for a model based on 750 subjects from centers b, c, and d, on 250 subjects from center a. Since the split is not at random, this qualifies as external validation. The final model is based on all data, and can subsequently be validated externally when new data become available for analysis after publication of the model. This approach is best when there is a large number of small centers.
See this image and copyright information in PMC

References

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