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Systematic Missing-At-Random (SMAR) Design and Analysis for Translational Research Studies

Author

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  • Belitskaya-Levy Ilana

    (New York University School of Medicine)

  • Shao Yongzhao

    (Iowa State University)

  • Goldberg Judith D

    (New York University School of Medicine)

Abstract

Translational research studies often involve a central study (e.g. clinical trial, cohort of patients, etc.) and multiple investigators who are each interested in addressing different research questions using the same patient population. However, it is often impossible for the investigators to include all patients in all of the ancillary translational research substudies that are part of the main study. This arises due to time and budgetary constraints and other logistical considerations. In this paper, we propose a prospective Systematic Missing-At-Random study design (SMAR) with planned partially missing covariates collected using a nested random sampling scheme that allows an integrated statistical analysis across all domains of data. We propose an algorithm for data analysis that incorporates the features of the design. We show that the SMAR design is computationally and statistically efficient as well as cost effective using simulation studies and a published data example. An extension to a two-stage prospective-retrospective design is discussed.

Suggested Citation

  • Belitskaya-Levy Ilana & Shao Yongzhao & Goldberg Judith D, 2008. "Systematic Missing-At-Random (SMAR) Design and Analysis for Translational Research Studies," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, July.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:15
    DOI: 10.2202/1557-4679.1046
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    References listed on IDEAS

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    1. Chatterjee N. & Chen Y-H. & Breslow N.E., 2003. "A Pseudoscore Estimator for Regression Problems With Two-Phase Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 158-168, January.
    2. Hua Yun Chen, 2003. "A note on the prospective analysis of outcome‐dependent samples," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 575-584, May.
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