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Statistical models for the natural h...

Salzman, Peter.

Statistical models for the natural history of breast cancer.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical models for the natural history of breast cancer.
作者:	Salzman, Peter.
面頁冊數:	62 p.
附註:	Advisers: Sylvia K. Plevritis; Peter W. Glynn.
附註:	Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0349.
Contained By:	Dissertation Abstracts International66-01B.
標題:	Statistics.
電子資源:	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.

Advisers: Sylvia K. Plevritis; Peter W. Glynn.

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:

182057
Statistics.

Statistical models for the natural history of breast cancer.
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245 1 0 $a Statistical models for the natural history of breast cancer.
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500 $a Advisers: Sylvia K. Plevritis; Peter W. Glynn.
500 $a Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0349.
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 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.
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.
590 $a School code: 0212.
650 # 0 $a Statistics. $3 182057
650 # 0 $a Health Sciences, Public Health. $3 212500
650 # 0 $a Operations Research. $3 227148
650 # 0 $a Health Sciences, Oncology. $3 226907
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