Optimal design for epidemiological studies subject to designed missingness |
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Authors: | Michele Morara Louise Ryan Andres Houseman Warren Strauss |
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Institution: | (1) Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201, USA;(2) Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA |
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Abstract: | In large epidemiological studies, budgetary or logistical constraints will typically preclude study investigators from measuring
all exposures, covariates and outcomes of interest on all study subjects. We develop a flexible theoretical framework that
incorporates a number of familiar designs such as case control and cohort studies, as well as multistage sampling designs.
Our framework also allows for designed missingness and includes the option for outcome dependent designs. Our formulation
is based on maximum likelihood and generalizes well known results for inference with missing data to the multistage setting.
A variety of techniques are applied to streamline the computation of the Hessian matrix for these designs, facilitating the
development of an efficient software tool to implement a wide variety of designs. |
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Keywords: | Stage-sampling design Validation sampling Constrained optimization |
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