Browsing by Subject "Effect size"
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Item Identification of optimal moderators in clinical trials(2015-05) Wang, Li, M.S. in Statistics; Daniels, Michael Joseph; Muller, PeterModerators and mediators can be very informative in the analysis of clinical trials to help determine what treatment should be assigned to individuals (moderators) and to determine how to improve treatments (mediators). It is well known that a treatment might not be equally beneficial to everyone and an overall effective treatment may be less effective (or even harmful) in certain groups; this highlights the importance of moderators in making treatment assignment decisions. A combined moderator, or optimal moderator, can be useful when multiple potential moderators exist, but no individual one is particularly strong. This report reviews how to assess a single moderator as well as approaches to derive an optimal moderator. An example from randomized clinical trial is presented, including the identification of an optimal moderator.Item Software implementation of modeling and estimation of effect size in multiple baseline designs(2013-12) Xu, Weiwei, active 2013; Beretvas, Susan NatashaA generalized design-comparable effect size modeling and estimation for multiple baseline designs across individuals has been proposed and evaluated by Restricted Maximum Likelihood method in a hierarchical linear model using R. This report evaluates the exact approach of the modeling and estimation by SAS. Three models (MB3, MB4 and MB5) with same fixed effects and different random effects are estimated by PROC MIXED procedure with REML method. The unadjusted size and adjusted effect size are then calculated by matrix operation package PROC IML. The estimations for the fixed effects of the three models are similar to each other and to that of R. The variance components estimated by the two software packages are fairly close for MB3 and MB4, but the results are different for MB5 which exhibits boundary conditions for variance-covariance matrix. This result suggests that the nlme library in R works differently than the PROC MIXEDREML method in SAS under extreme conditions.