Open Access. Powered by Scholars. Published by Universities.®
Design of Experiments and Sample Surveys Commons™
Open Access. Powered by Scholars. Published by Universities.®
Johns Hopkins University, Dept. of Biostatistics Working Papers
Causal inference; Censoring by death; Missing data; Potential outcomes; Principal stratification; Quantum mechanics
Articles 1 - 1 of 1
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 …