**Methods:** A common method used to capture the correlated endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a basket trial and investigate two popular prior specifications: an inverse-gamma prior on the basket-level variance and a uniform prior on the basket-level standard deviation.

**Results:** From our simulation study, we see the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (<0.5), this can lead to unacceptably high false positive rates (>40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that moves the mass of the variance parameter away from zero, such as the uniform prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior must be larger than 1.

**Conclusion:** Based on our results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a large density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior on the standard deviation.

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**Methods:** Here, we used data from the Medical Expenditures Panel Survey (MEPS) to develop new age-specific coefficients for self-rated health, activities of daily living (ADL), instrumental activities of daily living (IADL), and the SF-12 physical function scale (PCS). We computed new age-specific transformations for ages 0 through 85 and compared the new transformations with published transformations for persons aged 65 and older.

**Results: **The transformed values were different at different ages, The new transformed values for persons 65 and over were remarkably similar to the published results, calculated from different datasets.

**Conclusion: **The new transformation equations should be particularly useful for studies involving persons younger than 65. For older persons, either the published equations or these new equations may be used.