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Discrete proportional hazards models...

Meier, Amalia Sophia.

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Discrete proportional hazards models for uncertain outcomes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Discrete proportional hazards models for uncertain outcomes./
作者:	Meier, Amalia Sophia.
面頁冊數:	101 p.
附註:	Source: Dissertation Abstracts International, Volume: 62-12, Section: B, page: 5672.
Contained By:	Dissertation Abstracts International62-12B.
標題:	Health Sciences, Public Health. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3036509
ISBN:	0493495371

Discrete proportional hazards models for uncertain outcomes.
Meier, Amalia Sophia.

Discrete proportional hazards models for uncertain outcomes. - 101 p.

Source: Dissertation Abstracts International, Volume: 62-12, Section: B, page: 5672.

Thesis (Ph.D.)--University of Washington, 2001.

Estimation of failure time is typically done when failure is observed with certainty. However, when false positive and false negative test results occur, a time to event analysis is also of interest. A procedure is desired that addresses information loss due to imperfect sensitivity/specificity in the outcome. Richardson and Hughes (2000) provide an algorithm for estimating the product limit of discrete time data with uncertain outcomes. Following their work, we develop a method that permits the estimation of both the survival curve and covariate effects. We show that the greatest bias in both hazard and covariate effect estimation stems from failure to account for false positive tests. However, simulations show that correct specification of these false testing rates permit unbiased estimation of both the baseline hazard and covariate effects. This method involves numerical maximization of a marginal likelihood. Inference is achieved through the asymptotic distribution of maximum likelihood estimates. We present a graphical comparison to discrete proportional hazards models, and evaluate the robustness of the estimates with regard to misspecification of the false testing rates.

ISBN: 0493495371Subjects--Topical Terms:

1017659
Health Sciences, Public Health.

Discrete proportional hazards models for uncertain outcomes.
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502 $a Thesis (Ph.D.)--University of Washington, 2001.
520 $a Estimation of failure time is typically done when failure is observed with certainty. However, when false positive and false negative test results occur, a time to event analysis is also of interest. A procedure is desired that addresses information loss due to imperfect sensitivity/specificity in the outcome. Richardson and Hughes (2000) provide an algorithm for estimating the product limit of discrete time data with uncertain outcomes. Following their work, we develop a method that permits the estimation of both the survival curve and covariate effects. We show that the greatest bias in both hazard and covariate effect estimation stems from failure to account for false positive tests. However, simulations show that correct specification of these false testing rates permit unbiased estimation of both the baseline hazard and covariate effects. This method involves numerical maximization of a marginal likelihood. Inference is achieved through the asymptotic distribution of maximum likelihood estimates. We present a graphical comparison to discrete proportional hazards models, and evaluate the robustness of the estimates with regard to misspecification of the false testing rates.
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