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Statistical models for the natural h...

Salzman, Peter.

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Statistical models for the natural history of breast cancer.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical models for the natural history of breast cancer./
Author:	Salzman, Peter.
Description:	62 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0349.
Contained By:	Dissertation Abstracts International66-01B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3162333
ISBN:	0496962205

Statistical models for the natural history of breast cancer.
Salzman, Peter.

Statistical models for the natural history of breast cancer. - 62 p.

Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0349.

Thesis (Ph.D.)--Stanford University, 2005.

Breast cancer is one of the most common forms of cancer in US women. It is believed that early detection by screening mammography can reduce breast cancer mortality. To understand the impact of early detection, we need to understand the behavior of the tumor and its metastasis as a function of time. We develop two models for the natural history of breast cancer. One is used to estimate the distribution of the tumor doubling time for tumors that were detected in metastatic stage by relating the size of the detected tumor and the breast cancer survival.

ISBN: 0496962205Subjects--Topical Terms:

517247
Statistics.

Statistical models for the natural history of breast cancer.
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100 1 $a Salzman, Peter. $3 1908718
245 1 0 $a Statistical models for the natural history of breast cancer.
300 $a 62 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0349.
500 $a Advisers: Sylvia K. Plevritis; Peter W. Glynn.
502 $a Thesis (Ph.D.)--Stanford University, 2005.
520 $a Breast cancer is one of the most common forms of cancer in US women. It is believed that early detection by screening mammography can reduce breast cancer mortality. To understand the impact of early detection, we need to understand the behavior of the tumor and its metastasis as a function of time. We develop two models for the natural history of breast cancer. One is used to estimate the distribution of the tumor doubling time for tumors that were detected in metastatic stage by relating the size of the detected tumor and the breast cancer survival.
520 $a The second model can be used to explain the trends in mortality. This model relates the size of the tumor to its stage at the time of detection. This model can be used to measure the stage shift introduced by early detection programs. Stage shift implies a better breast cancer survival, which may lead to a reduction in breast cancer mortality.
520 $a In the last chapter we study a general nonlinear regression model with censoring. This model is a generalization of the estimation method used in the growth rate model. We propose an estimator derived from an estimating equation. We use the theory of empirical processes to obtain uniform limit theorems and prove the consistency and asymptotic normality of the estimator.
590 $a School code: 0212.
650 4 $a Statistics. $3 517247
650 4 $a Health Sciences, Public Health. $3 1017659
650 4 $a Operations Research. $3 626629
650 4 $a Health Sciences, Oncology. $3 1018566
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0463
690 $a 0573
690 $a 0796
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690 $a 0308
710 2 0 $a Stanford University. $3 754827
773 0 $t Dissertation Abstracts International $g 66-01B.
790 1 0 $a Plevritis, Sylvia K., $e advisor
790 1 0 $a Glynn, Peter W., $e advisor
790 $a 0212
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3162333