Differentiate between validation and calibration. How can we perform validation of a model?

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Suresh Chand · Accepted Answer

Validation is the process that ensures that a system, product, or service consistently provides results within acceptable criteria.  The performance, quality, and other operating parameters are tested to verify that they comply with the necessary requirements. Usually, process validation is done by a third party to make sure that the buyer is given a product that meets the specifications, requirements, and accepted standards.  Documented results must be produced at the end of the process. The major differences between validation and calibration are CALIBRATION VALIDATION Calibration is a demonstration that, a particular instrument or device produces results within specified limits by comparisons with those produced by a reference or traceable standard over an appropriate range of measurements. Validation is a documented program that provides a high degree of assurance that a specific process, equipment, method or system consistently produces a result meeting pre-determined acceptance criteria. Calibration ensures that instruments or measuring devices produce accurate results. Validation provides documented evidence that a process, equipment, method, or system produces consistent results (in other words, it ensures that uniform batches are produced). In calibration performance of an instrument or device is compared against a reference standard. No such reference standards are used in the validation program. Shall be performed periodically, to identify the ‘drift’ of the measuring device or equipment and make them accurate. No such requirements. Shall be performed when changes or modifications happen to the existing system or once the revalidation period is reached. Shall be performed as per calibration SOP. Shall be performed as per validation protocol. Naylor and Finger proposed three step approach for the validation of the model. These steps are as follows: Face validity - A model should appear reasonable on its face to model users and to those who know about the real system that is being simulated. - A model should be designed with a high degree of realism regarding system structure and behavior through reliable data. - The potential users should also be involved in the validation process to aid in the identification of model deficiencies and optimizing those deficiencies to produce a better model. This process is termed a structural walkthrough. - Sensitivity analysis is also used for the face validity of the model. It analyses the effect on output when there is a change in input parameters. Sensitivity analysis is done through appropriate statistical techniques. 2.  Model Assumptions Model assumptions fall into two categories: structural assumptions and data assumptions. Structural assumptions involve questions of how the system operates and usually involve simplification and abstractions of reality. Data assumptions should be based on the collection of reliable data and correct statistical analysis of the data. Does it deal with such questions as what kind of input data model is? What are the parameter values of the input data model? Input-Output Transformations - It involves validating whether the model can predict the future behavior of the real system when the model input data match the real inputs and when a policy implemented in the model is implemented at some point in the system. - In this validation phase, the model accepts values of the input parameters and transforms them into output measures of performance. - The modeler uses the historical data reserved for validation purposes only. - For the complete input-output validation, at least one set of input conditions should be collected from the system data so as to compare to the model prediction.

Differentiate between validation and calibration. How can we perform validation of a model?

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Differentiate between validation and calibration. How can we perform validation of a model?

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