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Conditional likelihood inference under complex ascertainment using data augmentation

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  • David Clayton

Abstract

In many applications, particularly in genetics, samples are drawn under complex ascertainment rules. For example, families may only be selected for study if two or more siblings have trait values exceeding some threshold. The correct likelihood for inference in such situations involves the probabilities of ascertainment, and these are frequently intractable. A consistent, but not fully efficient, method of analysis of such studies is proposed. The main idea is to augment the data with additional pseudo-observations simulated under the ascertainment scheme, and to analyse using a conditional likelihood for discrimination between true observations and pseudo-observations. Ascertainment probabilities cancel in this likelihood. The method is illustrated with a simple example involving left-truncated failure times. Copyright Biometrika Trust 2003, Oxford University Press.

Suggested Citation

  • David Clayton, 2003. "Conditional likelihood inference under complex ascertainment using data augmentation," Biometrika, Biometrika Trust, vol. 90(4), pages 976-981, December.
  • Handle: RePEc:oup:biomet:v:90:y:2003:i:4:p:976-981
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    Cited by:

    1. Janne Pitkäniemi & Sirkka-Liisa Varvio & Jukka Corander & Nella Lehti & Jukka Partanen & Eva Tuomilehto-Wolf & Jaakko Tuomilehto & Andrew Thomas & Elja Arjas, 2009. "Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-8, August.
    2. Jonathan S. Schildcrout & Shawn P. Garbett & Patrick J. Heagerty, 2013. "Outcome Vector Dependent Sampling with Longitudinal Continuous Response Data: Stratified Sampling Based on Summary Statistics," Biometrics, The International Biometric Society, vol. 69(2), pages 405-416, June.
    3. Jonathan S. Schildcrout & Patrick J. Heagerty, 2011. "Outcome-Dependent Sampling from Existing Cohorts with Longitudinal Binary Response Data: Study Planning and Analysis," Biometrics, The International Biometric Society, vol. 67(4), pages 1583-1593, December.
    4. Paulsen, Jostein & Lunde, Astrid & Skaug, Hans Julius, 2008. "Fitting mixed-effects models when data are left truncated," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 121-133, August.

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