title: Optimal design for epidemiological studies subject to designed missingness.
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.
authors: Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201, USA.
authors: Michele
authors: M
authors: Morara
authors: Louise
authors: L
authors: Ryan
authors: Andres
authors: A
authors: Houseman
authors: Warren
authors: W
authors: Strauss
mesh_terms: D000293:Adolescent; D001321:Autistic Disorder / Q000139:chemically induced / Q000175:diagnosis; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D015340:Epidemiologic Research Design; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D010575:Pesticides / Q000009:adverse effects; D012984:Software
chemical_list: D010575:Pesticides