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35A Robust bayesian analysis of clinical trials
56S
Abstracts achieve normality; 3) the identification of meaningful cutpoints; and 4) a comparison of analysis strategies when using J-shaped compliance data as an outcome variable and as an explanatory covariate. To illustrate these points, medication compliance dat~, collected longitudinallyfrom a trialin rheumatoid arthritis patients, will be considered. 35A ROBUST BAYESIAN ANALYSIS OF CLINICAL TRIALS Joel B. Greenhouse and Larry Wasserman
Carnegie Mellon University Pittsburgh, Pennsylvania The implementation and analysis of randomized controlled clinical trials in practice is rarely simple and frequently requires the development of innovative statistical methods. Bayesian methods for the analysis of clinical trial d ~ have received increasing attention as they offer an approach to dealing with difficult practical problems such as, the use of relevant historical information, when to stop a trial early, how to monitor a trial for unexpected outcomes, and how to assess clinically meaningful outcomes. A common criticism of the Bayesian approach has focused on the need to specify a prior distribution. In an attempt to address this criticism, we describe methods for assessing the robustness of the posterior distribution to the specification of the prior. A case study will be presented to illustrate the methodology. The emphasis of the talk will be on the use of robust Bayesian methods in practice. 36A EVALUATING THE IMPACT OF TREATMENT CROSSOVERS ON STATISTICAL ANALYSIS OF CLINICAL TRIALS Miehele Melia
The WdmerOpluOmlmolo~alInstitute The Johns Hopkins University Baltimore, Maryland Treatment crossovers, patients who receive one of the alternative ueatments in a clinical trial rather than the treatment to which they were randomly assigned, occur in most clinical trials. Standard practice is to include these patients in the group to which they were randomized during the primary statistical analysis, since this maintains the comparable groups achieved by randomization. Furthermore, as some investigators have shown, other methods for including treatment crossovers in the analysis can introduce serious bias into the study results (The Coronary Drug Project Research Group, 1980, Peduzzi, et al. 1993). This principle of analysis is known as "intent-to-treat" or "analysis as-randomized." However, the "analysisas-randomized" principle is sometimes criticized as not representing the "true" effect of treatment, especially by clinicians. Thus, in practice, many investigators carry out analyses using various other methods to account for treatment crossovers in addition to the asrandomized analysis.
Abstracts achieve normality; 3) the identification of meaningful cutpoints; and 4) a comparison of analysis strategies when using J-shaped compliance data as an outcome variable and as an explanatory covariate. To illustrate these points, medication compliance dat~, collected longitudinallyfrom a trialin rheumatoid arthritis patients, will be considered. 35A ROBUST BAYESIAN ANALYSIS OF CLINICAL TRIALS Joel B. Greenhouse and Larry Wasserman
Carnegie Mellon University Pittsburgh, Pennsylvania The implementation and analysis of randomized controlled clinical trials in practice is rarely simple and frequently requires the development of innovative statistical methods. Bayesian methods for the analysis of clinical trial d ~ have received increasing attention as they offer an approach to dealing with difficult practical problems such as, the use of relevant historical information, when to stop a trial early, how to monitor a trial for unexpected outcomes, and how to assess clinically meaningful outcomes. A common criticism of the Bayesian approach has focused on the need to specify a prior distribution. In an attempt to address this criticism, we describe methods for assessing the robustness of the posterior distribution to the specification of the prior. A case study will be presented to illustrate the methodology. The emphasis of the talk will be on the use of robust Bayesian methods in practice. 36A EVALUATING THE IMPACT OF TREATMENT CROSSOVERS ON STATISTICAL ANALYSIS OF CLINICAL TRIALS Miehele Melia
The WdmerOpluOmlmolo~alInstitute The Johns Hopkins University Baltimore, Maryland Treatment crossovers, patients who receive one of the alternative ueatments in a clinical trial rather than the treatment to which they were randomly assigned, occur in most clinical trials. Standard practice is to include these patients in the group to which they were randomized during the primary statistical analysis, since this maintains the comparable groups achieved by randomization. Furthermore, as some investigators have shown, other methods for including treatment crossovers in the analysis can introduce serious bias into the study results (The Coronary Drug Project Research Group, 1980, Peduzzi, et al. 1993). This principle of analysis is known as "intent-to-treat" or "analysis as-randomized." However, the "analysisas-randomized" principle is sometimes criticized as not representing the "true" effect of treatment, especially by clinicians. Thus, in practice, many investigators carry out analyses using various other methods to account for treatment crossovers in addition to the asrandomized analysis.