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Incorporating diagnostic accuracy into the estimation of discrete survival function

Author

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  • Abidemi K. Adeniji
  • Steven H. Belle
  • Abdus S. Wahed

Abstract

Empirical distribution function (EDF) is a commonly used estimator of population cumulative distribution function. Survival function is estimated as the complement of EDF. However, clinical diagnosis of an event is often subjected to misclassification, by which the outcome is given with some uncertainty. In the presence of such errors, the true distribution of the time to first event is unknown. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values of the prediction process into a product-limit style construction. This will allow us to quantify the bias of the EDF estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival rates and its variance. Asymptotic properties of the proposed estimators are provided and these properties are examined through simulations. We evaluate our methods using data from the VIRAHEP-C study.

Suggested Citation

  • Abidemi K. Adeniji & Steven H. Belle & Abdus S. Wahed, 2014. "Incorporating diagnostic accuracy into the estimation of discrete survival function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 60-72, January.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:60-72
    DOI: 10.1080/02664763.2013.830087
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    References listed on IDEAS

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    1. Amalia S. Meier & Barbra A. Richardson & James P. Hughes, 2003. "Discrete Proportional Hazards Models for Mismeasured Outcomes," Biometrics, The International Biometric Society, vol. 59(4), pages 947-954, December.
    2. John M. Neuhaus, 2002. "Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification," Biometrics, The International Biometric Society, vol. 58(3), pages 675-683, September.
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    Cited by:

    1. Hee-Koung Joeng & Ming-Hui Chen & Sangwook Kang, 2016. "Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 38-62, January.
    2. Hee-Koung Joeng & Abidemi K. Adeniji & Naitee Ting & Ming-Hui Chen, 2022. "Estimation of Discrete Survival Function through Modeling Diagnostic Accuracy for Mismeasured Outcome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 105-138, April.

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