For each response, separate linear mixed models that include both fixed and random effects will be applied. To meet the distributional assumptions of this model, responses may be transformed prior to model fitting. Fixed effects that will be included in the model are clinically relevant baseline and time dependent covariates and two way interactions as well as linear or quadratic time trends. In particular, a covariate for treatment arm, an indicator variable to define patients who chose the decision analytic model based treatment choice, and an interaction between these two variables will be included. In this case, the interaction will allow us to test if the effect that knowledge of the decision analytic model has on various responses is different for patients who chose the decision analytic model treatment choice compared to patients who did not chose the decision analytic model treatment choice. A covariate for the difference between the number of quality adjusted life years based on the decision analysis model and the final treatment choice and an interaction between this variable and the treatment arm indicator variable will also be included in the model to examine if the effect that knowledge of the decision analytic model has on various responses is effected by the theoretical degree of mistake. To fully account for the variation in subject specific trajectories over time, a random intercept will be included in this model although linear and quadratic random effects will also be considered if appropriate. In addition, the residual covariance structure for each model will also be investigated to avoid misspecification and invalid inferences. In particular, correct specification of the covariance structure will be investigated using different ways to model the measurement error (due to variation in the measurement of the response) and serial correlation (due to decreasing correlation between measurements over time). For instance, structures for serial correlation will be investigated using a semi-variogram. Although the problem of a misspecified covariance structure can and will be avoided by using a robust variance estimator when making inferences about the fixed effects of interest based on the marginal model, misspecification also leads to an inability to properly account for the effect of missing data, a common occurrence in longitudinal studies. Note that once an appropriate model is fit to each response of interest, inference regarding the fixed treatment and covariate effects on the response will be based on marginal models using the robust variance estimator to protect against model misspecification of the covariance structure. Statistical significance of fixed effects will be based on a 0.05 significance level. As this study is exploratory, no attempt will be made to account for multiple comparisons.