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This paper summarizes a "Plan, Do, Say" approach to validation that can be incorporated into statistical training so that biostatisticians can understand and implement validation principles in their research.
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Such an assessment can be achieved by implementing validation methods employing design-based inference in which the independent reference data are selected via a probability sampling design.
Thereby, the integration between the implemented validation and any authoring tool consist simply in creating an adapter that converts the tool model to the Validator Model and ensures that both are synchronized.
We implement a validation technique, using a polarization-encoding architecture based on digital speckle correlation and a binary key code.
We discuss the implications of applying these approaches and provide recommendations to researchers, policy-makers, and program leaders who implement QRIS validation studies.
The modelling language (eXtensible Markup Language, XML) allow to implement model validation as well as to easily transform information coming from / going to different software tools, achieving tool integration.
There are several tools that implement this validation method.
Mirroring these survey results, 20 laboratories believed that they need to implement more validation steps.
A striking finding was that 2/3 of all laboratories stated that they believed they needed to implement more validation.
To implement this validation step, we divided the 191 848 adults who are non-missing on the measure of severe depression, on a state-by-state basis, into two equal-sized random halves: test sample and validation sample.
Within OpenTox we have also implemented basic validation routines, simple validation (with supplied test set or training/test split), cross-validation routines (including leave-one-out), as well as making initial reporting routines available.
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