Optimal design for epidemiological studies subject to designed missingness
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| Publicado en: | Lifetime Data Analysis vol. 13, no. 4 (Dec 2007), p. 583-605 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1007/s10985-007-9068-7 |2 doi | |
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| 100 | 1 | |a Morara, Michele | |
| 245 | 1 | |a Optimal design for epidemiological studies subject to designed missingness | |
| 260 | |b Springer Nature B.V. |c Dec 2007 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 650 | 2 | 2 | |a Adolescent |
| 650 | 2 | 2 | |a Autistic Disorder |x chemically induced |
| 650 | 2 | 2 | |a Autistic Disorder |x diagnosis |
| 650 | 2 | 2 | |a Case-Control Studies |
| 650 | 2 | 2 | |a Child |
| 650 | 2 | 2 | |a Child, Preschool |
| 650 | 1 | 2 | |a Epidemiologic Research Design |
| 650 | 2 | 2 | |a Humans |
| 650 | 2 | 2 | |a Logistic Models |
| 650 | 2 | 2 | |a Longitudinal Studies |
| 650 | 2 | 2 | |a Pesticides |x adverse effects |
| 650 | 2 | 2 | |a Software |
| 653 | |a Epidemiology | ||
| 653 | |a Sampling | ||
| 653 | |a Optimization | ||
| 653 | |a Studies | ||
| 653 | |a Public health | ||
| 653 | |a Design optimization | ||
| 653 | |a Power | ||
| 653 | |a Generalized linear models | ||
| 653 | |a Missing data | ||
| 653 | |a Cohort analysis | ||
| 700 | 1 | |a Ryan, Louise | |
| 700 | 1 | |a Houseman, Andres | |
| 700 | 1 | |a Strauss, Warren | |
| 773 | 0 | |t Lifetime Data Analysis |g vol. 13, no. 4 (Dec 2007), p. 583-605 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/204474485/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/204474485/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/204474485/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |