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Use of Additional Information for Current Status Data with Two Competing Risks and Missing Failure Types

Author

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  • Tamalika Koley

    (Indian Institute of Management Lucknow)

  • Anup Dewanji

    (Inidan Statistical Institute)

Abstract

In practice, the failure type for some subjects may be missing or uncertain in competing risks data. Analysis of such uncertain failure type in current status data with two competing risks suffers from issues of model identifiability and requires some model assumptions to deal with that (Koley and Dewanji. Journal of Applied Statistics 49(7), (2022)). In this work, we attempt to alleviate this identifiability problem using some additional information and without any such model assumption. In particular, we consider additional information in the form of some prior knowledge on the missing probabilities. Next, we briefly discuss another type of additional information from a validation sample which ascertains failure type. We consider parametric estimation of the model parameters and non-parametric estimation of the sub-distribution functions. We investigate the associated large sample properties theoretically and the finite sample properties through simulation. We also consider analysis of a real data set for the purpose of illustration.

Suggested Citation

  • Tamalika Koley & Anup Dewanji, 2024. "Use of Additional Information for Current Status Data with Two Competing Risks and Missing Failure Types," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 477-505, November.
    • Handle: RePEc:spr:sankhb:v:86:y:2024:i:2:d:10.1007_s13571-024-00337-9
    DOI: 10.1007/s13571-024-00337-9
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    References listed on IDEAS

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    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    2. Anup Dewanji & Debasis Sengupta, 2003. "Estimation of Competing Risks with General Missing Pattern in Failure Types," Biometrics, The International Biometric Society, vol. 59(4), pages 1063-1070, December.
    3. Nicholas P. Jewell, 2003. "Nonparametric estimation from current status data with competing risks," Biometrika, Biometrika Trust, vol. 90(1), pages 183-197, March.
    4. Shuangge Ma & Michael Kosorok, 2006. "Adaptive penalized M-estimation with current status data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 511-526, September.
    5. Michael G. Hudgens & Glen A. Satten & Ira M. Longini, 2001. "Nonparametric Maximum Likelihood Estimation for Competing Risks Survival Data Subject to Interval Censoring and Truncation," Biometrics, The International Biometric Society, vol. 57(1), pages 74-80, March.
    6. Tamalika Koley & Anup Dewanji, 2019. "Revisiting Non-Parametric Maximum Likelihood Estimation of Current Status Data with Competing Risks," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 39-59, June.
    7. Radu V. Craiu, 2004. "Inference based on the EM algorithm for the competing risks model with masked causes of failure," Biometrika, Biometrika Trust, vol. 91(3), pages 543-558, September.
    8. Tamalika Koley & Anup Dewanji, 2022. "Current status data with two competing risks and missing failure types: a parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(7), pages 1769-1783, May.
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