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本文引用的文献

1
Breast Cancer Survival Analysis: Applying the Generalized Gamma Distribution under Different Conditions of the Proportional Hazards and Accelerated Failure Time Assumptions.乳腺癌生存分析：在比例风险和加速失效时间假设的不同条件下应用广义伽马分布
Int J Prev Med. 2012 Sep;3(9):644-51.
2
Applying conventional and saturated generalized gamma distributions in parametric survival analysis of breast cancer.将传统和饱和广义伽马分布应用于乳腺癌的参数生存分析。
Asian Pac J Cancer Prev. 2012;13(5):1829-31. doi: 10.7314/apjcp.2012.13.5.1829.
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Generalized log-gamma regression models with cure fraction.
Lifetime Data Anal. 2009 Mar;15(1):79-106. doi: 10.1007/s10985-008-9096-y. Epub 2008 Aug 27.
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[Variables associated with breast cancer in clients of primary healthcare units].[基层医疗单位患者中与乳腺癌相关的变量]
Cad Saude Publica. 2007 May;23(5):1061-9. doi: 10.1590/s0102-311x2007000500008.
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Confidence limits for probability of response in multistage phase II clinical trials.
Biometrics. 1985 Sep;41(3):741-4.

重新参数化的广义伽马部分线性回归及其在乳腺癌数据中的应用。

Reparametrized generalized gamma partially linear regression with application to breast cancer data.

作者信息

Fidelis Cleanderson R, Ortega Edwin M M, Prataviera Fábio, Vila Roberto, Cordeiro Gauss M

机构信息

ESALQ, Universidade de São Paulo, Piracicaba, Brazil.

Departamento de Estatística, Universidade de Brasília, Brasília, Brazil.

出版信息

J Appl Stat. 2024 Apr 2;51(15):3248-3265. doi: 10.1080/02664763.2024.2337086. eCollection 2024.

DOI:10.1080/02664763.2024.2337086
PMID:39507210
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11536640/
Abstract

We construct a new partially linear regression based on a reparametrized generalized gamma distribution with two systematic components that can be easily interpreted. Its parameters are estimated by penalized maximum likelihood. For different parameter settings, sample sizes, and censoring percentages, some simulations are performed to examine the accuracy of the maximum likelihood estimators, and the empirical distribution of the residuals compared with the standard normal distribution. The methodology is applied to breast cancer data in the city of João Pessoa in the state of Paraíba in Brazil.

摘要

我们基于重新参数化的广义伽马分布构建了一种新的部分线性回归，该分布具有两个易于解释的系统成分。其参数通过惩罚最大似然法进行估计。针对不同的参数设置、样本量和删失百分比，进行了一些模拟以检验最大似然估计量的准确性，并将残差的经验分布与标准正态分布进行比较。该方法应用于巴西帕拉伊巴州若昂佩索阿市的乳腺癌数据。