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The skewed, non-negative distribution of cost variables was analysed using the gamma regression and the two-part models, and model adjustment for potential confounding was performed using the predictive margins and predictive distributions in the frequentist and the Bayesian inferences, respectively.
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For correct viewing on an SGI, set your gamma correction value (using the "gamma" command) to 1.7/1.4 = 1.2.
Rate heterogeneity among sites was modelled using the gamma model.
The priors were set using the gamma-Dirichlet prior [ 56].
However, the Gamma regression model showed minimum MSE values.
However, the Gamma regression model had the smallest biases across all data-generating processes.
Based on our result, the Gamma regression model provided more accurate estimates of population mean.
Monthly treatment costs during the follow-up period were compared using generalized gamma regression controlling for pre-index costs in patients with positive post-index healthcare costs.
All-cause non-DMT costs in 2010 between patients with HRA and those without HRA were compared using a gamma regression with log link model, controlling for the same variables mentioned above.
Our sensitivity analyses using generalized gamma regression, beta regression and Dirichlet regression were consistent with the findings obtained by linear regression.
Comparisons of all-cause costs (non-DMT costs) were also performed using a gamma regression with log link model in 2010 between patients with HRA and those without HRA, controlling for the same variables mentioned above.
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