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Slide 1An Introduction to Visual Predictive Checks Nick Oxford Department of Pharmacology & Clinical Pharmacology University of AucklandSlide 2Outline What is a Visual Predictive Check? What choices
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How to fill out visual predictive checks:

01
Start by gathering the necessary data for analysis.
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Ensure that you have a clear understanding of the objectives of the visual predictive checks.
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Select an appropriate software or tool to perform the visual predictive checks.
04
Input the gathered data into the software or tool.
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Choose the appropriate models or algorithms for the analysis.
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Run the analysis using the selected models or algorithms.
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Evaluate the results obtained from the visual predictive checks.
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Interpret the findings and draw meaningful conclusions.
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Document the process, results, and conclusions for future reference.

Who needs visual predictive checks:

01
Pharmaceutical companies: Visual predictive checks are crucial in drug development and evaluation. They help assess the effectiveness and safety of new medications before they are released to the market.
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Clinical researchers: Visual predictive checks aid in the analysis of clinical trial data, allowing researchers to understand the behavior of the tested drugs and make informed decisions based on the results.
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Regulatory agencies: Visual predictive checks are used by regulatory authorities to evaluate the safety and effectiveness of drugs submitted for approval. It helps them assess whether the drug meets the required standards and whether it poses any potential risks to patients.
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Data scientists and statisticians: Visual predictive checks provide valuable insights into the predictive performance of statistical models and help improve the accuracy of predictions in various fields such as finance, marketing, and epidemiology.
05
Quality control teams: Visual predictive checks are essential in manufacturing processes to identify any deviations from the expected outcomes and ensure products meet the required quality standards.

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Visual predictive checks (VPC) are graphical tools used to evaluate the predictive performance of pharmacometric models by comparing observed data with model predictions.
Pharmacometricians, researchers, and scientists involved in drug development are required to file visual predictive checks.
Visual predictive checks can be filled out by plotting observed data against model predictions and assessing the agreement between the two.
The purpose of visual predictive checks is to assess the predictive performance of pharmacometric models and identify any potential model misspecification.
On visual predictive checks, information such as observed data, model predictions, and measures of agreement between the two must be reported.
The deadline to file visual predictive checks in 2023 is typically at the end of the reporting period for the specific study or project.
The penalty for the late filing of visual predictive checks may vary depending on the regulating body or organization overseeing the study or project.
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