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Optimal design for epidemiological studies subject to designed missingness

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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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Authors and Affiliations

  1. Battelle Memorial Institute, 505 King Avenue, Columbus, OH, 43201, USA

    Michele Morara & Warren Strauss

  2. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA

    Louise Ryan & Andres Houseman

Authors
  1. Michele Morara
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  2. Louise Ryan
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  3. Andres Houseman
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  4. Warren Strauss
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Correspondence to Louise Ryan.

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Morara, M., Ryan, L., Houseman, A. et al. Optimal design for epidemiological studies subject to designed missingness. Lifetime Data Anal 13, 583–605 (2007). https://doi.org/10.1007/s10985-007-9068-7

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  • DOI: https://doi.org/10.1007/s10985-007-9068-7

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