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No adaptive design is available for continuous responses in the presence of prognostic factors, which is not model based.
The course begins with a review of linear models for continuous responses and then considers logistic regression models for binary data and log-linear models for count data, including rates and contingency tables and hazard models for duration data.
Quantile regression was originally developed for continuous responses as count responses do not have continuous quantiles.
For continuous responses, I used multi-level mixed-effects linear regressions (xtmixed).
For continuous responses Bland Altman plots were used to inspect for differences in variability between methods and the effect of the order of interview methods [28].
Means and standard deviations (SD) were calculated for continuous responses, and frequencies were calculated for categorical responses.
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In practice, it is important to find optimal allocation strategies for continuous response with multiple treatments under some optimization criteria.
For continuous response, the loss function can be Gaussian or Laplace functions.
For continuous response variable, the appropriate loss function is the squared-error L2 loss and its derivative represents the residual.
For continuous response variables, the quantile residual is then easily defined by: r i = Φ − 1 F y i ; μ ^ i, ϕ ^ (5).
Standard choices are the logarithmic score and the Brier score for binary response variables [9] and the continuous rank probability score for continuous response variables [10].
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