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osDesign: An R Package for the Analysis, Evaluation, and Design of Two-Phase and Case-Control Studies

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  • Haneuse, Sebastien
  • Saegusa, Takumi
  • Lumley, Thomas

Abstract

The two-phase design has recently received attention in the statistical literature as an extension to the traditional case-control study for settings where a predictor of interest is rare or subject to missclassification. Despite a thorough methodological treatment and the potential for substantial efficiency gains, the two-phase design has not been widely adopted. This may be due, in part, to a lack of general-purpose, readily-available software. The osDesign package for R provides a suite of functions for analyzing data from a two-phase and/or case-control design, as well as evaluating operating characteristics, including bias, efficiency and power. The evaluation is simulation-based, permitting flexible application of the package to a broad range of scientific settings. Using lung cancer mortality data from Ohio, the package is illustrated with a detailed case-study in which two statistical goals are considered: (i) the evaluation of small-sample operating characteristics for two-phase and case-control designs and (ii) the planning and design of a future two-phase study.

Suggested Citation

  • Haneuse, Sebastien & Saegusa, Takumi & Lumley, Thomas, 2011. "osDesign: An R Package for the Analysis, Evaluation, and Design of Two-Phase and Case-Control Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i11).
    • Handle: RePEc:jss:jstsof:v:043:i11
    DOI: http://hdl.handle.net/10.18637/jss.v043.i11
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    Cited by:

    1. Rong Fu & Peter B. Gilbert, 2017. "Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 136-159, January.
    2. Lee, Unkyung & Sun, Yanqing & Scheike, Thomas H. & Gilbert, Peter B., 2018. "Analysis of generalized semiparametric regression models for cumulative incidence functions with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 59-79.

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