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A note on the prospective analysis of outcome‐dependent samples

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  • Hua Yun Chen

Abstract

Summary. Two likelihood representations corresponding to the prospective and retrospective analyses of the case–control design are derived for general outcome‐dependent samples with arbitrary discrete or continuous outcomes and possibly non‐multiplicative models. Parameter identification in the general outcome‐dependent design is reduced to the simple problem of parameter identification in the general odds ratio function. Both likelihoods are shown to generate the same profile likelihood for the common parameter of interest. Maximum like‐ lihood estimators based on either likelihood are semiparametric efficient for the identifiable parameters.

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  • 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.
    • Handle: RePEc:bla:jorssb:v:65:y:2003:i:2:p:575-584
    DOI: 10.1111/1467-9868.00403
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    Cited by:

    1. Chen, Hua Yun, 2010. "Compatibility of conditionally specified models," Statistics & Probability Letters, Elsevier, vol. 80(7-8), pages 670-677, April.
    2. Hua Yun Chen, 2007. "A Semiparametric Odds Ratio Model for Measuring Association," Biometrics, The International Biometric Society, vol. 63(2), pages 413-421, June.
    3. Hua Yun Chen & Hui Xie & Yi Qian, 2011. "Multiple Imputation for Missing Values through Conditional Semiparametric Odds Ratio Models," Biometrics, The International Biometric Society, vol. 67(3), pages 799-809, September.
    4. 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 1-28, July.
    5. Hua Yun Chen & Daniel E. Rader & Mingyao Li, 2015. "Likelihood Inferences on Semiparametric Odds Ratio Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1125-1135, September.
    6. Christopher Vahl & Qing Kang, 2015. "Analysis of an outcome-dependent enriched sample: hypothesis tests," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 387-409, September.
    7. Grünewald Maria & Humphreys Keith & Hössjer Ola, 2010. "A Stochastic EM Type Algorithm for Parameter Estimation in Models with Continuous Outcomes, under Complex Ascertainment," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-31, July.

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