Imagen de Portada

Optimal design for epidemiological studies subject to designed missingness

Guardado en:
Publicado en:Lifetime Data Analysis vol. 13, no. 4 (Dec 2007), p. 583-605
Autor principal: Morara, Michele
Otros Autores: Ryan, Louise, Houseman, Andres, Strauss, Warren
Publicado:
Springer Nature B.V.
Materias:
Adolescent
Autistic Disorder > chemically induced
Autistic Disorder > diagnosis
Case-Control Studies
Child
Child, Preschool
Epidemiologic Research Design
Humans
Logistic Models
Longitudinal Studies
Pesticides > adverse effects
Software
Epidemiology
Sampling
Optimization
Studies
Public health
Design optimization
Power
Generalized linear models
Missing data
Cohort analysis
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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. [PUBLICATION 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.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-007-9068-7
Fuente:ABI/INFORM Global