The rapidly increasing cost to develop new drugs calls for new tools that efficiently enable the demonstration of the safety and effectiveness of a new drug. When validating such a decision-enabling tool, a traditional approach is typically to apply the tool on a positive control, known to be effective, and ascertain that a statistically significant effect is obtained. We argue, however, that the validation study should be designed to show that the tool provides a variability that is small in relation to the treatment effect, which means that the tool has the capacity of providing decision-enabling results in small-sample studies in routine use.
We give details on the relevant test to perform in the validation of a decision-enabling tool and use the development of a human pharmacological model, aimed at studying neuropathic pain in 2 × 2 crossover trials, as a motivating example. We also develop power and sample size calculations, and illustrate the implications on sample size needed for a validation study. Results show that to obtain pertinent evidence that the decision-enabling tool is useful, that is, to reject the relevant null hypothesis, a substantially increased sample size would often be needed in the validation study, as compared to traditional approaches.
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The authors have disclosed that they are employees of AstraZeneca.
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Sjögren, N., Wiklund, S.J. Validation of Decision-enabling Tools: Showing That the Model Is Useful. Ther Innov Regul Sci 45, 759–765 (2011). https://doi.org/10.1177/009286151104500512
- Model validation
- Coeffcient of variation
- Relative standard deviation
- Effect size
- t distribution