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A nonidentifiability aspect of the problem of competing risks.

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Tsiatis, A·1975·Proceedings of the National Academy of Sciences

Abstract

For an experimental animal exposed to k greater than 1 possible risks of death R1, R2, ..., Rk, the term i-th potential survival time designates a random variable Yi supposed to represent the age at death of the animal in hypothetical conditions in which Ri is the only possible risk. The probability that Yi will exceed a preassigned t is called the i-th net survival probability. The results of a survival experiment are represented by k "crude" survival functions, empirical counterparts of the probabilities Qi(t) that an animal will survive at least up to the age t and eventually die from Ri. The analysis of a survival experiment aims at estimating the k net survival probabilities using the empirical data on those termed crude. Therorems 1 and 2 establish the relationship between the net and the crude probabilities of survival. In particular, Theorem 2 shows that, without the not directly verifiable assumption that in their joint distribution the variables Y1, Y2, ..., Yk are mutually independent, a given set of crude survival probabilities Qi(t) does not identify the corresponding net probabilities. An example shows that the results of a customary method of analysis, based on the assumption that Y1, Y2, ..., Yk are independent, may have no resemblance to reality.

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Metadata

Authors
Tsiatis, A.
Venue
Proceedings of the National Academy of Sciences
Published
1975-01
Volume
72
Issue
1
Pages
20-22
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2024385229
Cited by
862
References
0
FWCI
18.27
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A nonidentifiability aspect of the problem of competing risks. https://app.cochat.ai/papers/10.1073/pnas.72.1.20
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Tsiatis, A. (1975). A nonidentifiability aspect of the problem of competing risks. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.72.1.20

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.