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Full-Text Articles in Design of Experiments and Sample Surveys
Principal Stratification Designs To Estimate Input Data Missing Due To Death, Constantine E. Frangakis, Donald B. Rubin, Ming-Wen An, Ellen Mackenzie
Principal Stratification Designs To Estimate Input Data Missing Due To Death, Constantine E. Frangakis, Donald B. Rubin, Ming-Wen An, Ellen Mackenzie
Johns Hopkins University, Dept. of Biostatistics Working Papers
We consider studies of cohorts of individuals after a critical event, such as an injury, with the following characteristics. First, the studies are designed to measure “input” variables, which describe the period before the critical event, and to characterize the distribution of the input variables in the cohort. Second, the studies are designed to measure “output” variables, primarily mortality after the critical event, and to characterize the predictive (conditional) distribution of mortality given the input variables in the cohort. Such studies often possess the complication that the input data are missing for those who die shortly after the critical event …
Designs In Partially Controlled Studies: Messages From A Review, Fan Li, Constantine E. Frangakis
Designs In Partially Controlled Studies: Messages From A Review, Fan Li, Constantine E. Frangakis
Johns Hopkins University, Dept. of Biostatistics Working Papers
The ability to evaluate effects of factors on outcomes is increasingly important for a class of studies that control some but not all of the factors. Although important advances have been made in methods of analysis for such partially controlled studies,work on designs for such studies has been relatively limited. To help understand why, we review main designs that have been used for such partially controlled studies. Based on the review, we give two complementary reasons that explain the limited work on such designs, and suggest a new direction in this area.
Bayesian Geostatistical Design, Peter J. Diggle, Soren Lophaven
Bayesian Geostatistical Design, Peter J. Diggle, Soren Lophaven
Johns Hopkins University, Dept. of Biostatistics Working Papers
This paper describes the use of model-based geostatistics for choosing the optimal set of sampling locations, collectively called the design, for a geostatistical analysis. Two types of design situations are considered. These are retrospective design, which concerns the addition of sampling locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing optimal positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model parameter values are unknown. The results show that in this situation …
Optimal Sample Size For Multiple Testing: The Case Of Gene Expression Microarrays, Peter Muller, Giovanni Parmigiani, Christian Robert, Judith Rousseau
Optimal Sample Size For Multiple Testing: The Case Of Gene Expression Microarrays, Peter Muller, Giovanni Parmigiani, Christian Robert, Judith Rousseau
Johns Hopkins University, Dept. of Biostatistics Working Papers
We consider the choice of an optimal sample size for multiple comparison problems. The motivating application is the choice of the number of microarray experiments to be carried out when learning about differential gene expression. However, the approach is valid in any application that involves multiple comparisons in a large number of hypothesis tests. We discuss two decision problems in the context of this setup: the sample size selection and the decision about the multiple comparisons. We adopt a decision theoretic approach,using loss functions that combine the competing goals of discovering as many ifferentially expressed genes as possible, while keeping …