Estimating Unknown Transition Times Using a Piecewise Nonlinear Mixed-Effects Model in Men with Prostate Cancer
Estimating Unknown Transition Times Using a Piecewise Nonlinear Mixed-Effects Model in Men with Prostate Cancer
复制标题
使用分段非线性混合效应模型估计男性前列腺癌的未知转变时间
DOI：
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Larry J. Brant
中科院分区：
文献类型：
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作者：
Christopher H. Morrell;J. Pearson;H. Ballentine Carter;Larry J. Brant
摘要了解前列腺特异性抗原（PSA）水平何时开始开始快速升高，并确定局部局限性前列腺癌患者与转移性肿瘤患者PSA进展的自然史是否不同，可能具有临床意义。本文使用非线性混合效应模型来描述前列腺癌临床检测之前男性PSA的纵向变化。18例根据前列腺活检诊断为前列腺癌的受试者可重复测量PSA。PSA测量是在从检测到癌症之前至少10.0年和长达25.6年的观察的受试者中收集的重复冷冻血清样本上确定的。一个分段模型被用来描述这些数据。该模型在癌症被检测到之前很久是线性的，并且在癌症被检测到的时间附近是指数的。PSA水平从线性变为指数PSA进展的时间尚不清楚，但可以通过包括以下指标来估计。
Abstract It may be clinically useful to know when prostate-specific antigen (PSA) levels first begin to rise rapidly and to determine if the natural history of PSA progression is different in men with locally confined prostate cancers compared to men with metastatic tumors. This article uses a nonlinear mixed-effects model to describe longitudinal changes in PSA in men before their prostate cancers were detected clinically. Repeated measurements of PSA are available for 18 subjects with a diagnosis of prostate cancer based on prostate biopsy. PSA measurements were determined on repeated frozen serum samples collected from subjects with at least 10.0 years and up to 25.6 years of observation before the cancer was detected. A piecewise model is used to describe this data. The model is linear long before the cancer was detected and exponential nearer the time the cancer was detected. The time at which the PSA levels change from linear to exponential PSA progression is unknown but can be estimated by includin...
影响因子：
6.6
作者：
PARTIN, AW;CARTER, HB;WALSH, PC
通讯作者：
WALSH, PC
影响因子：
1.9
作者：
LINDSTROM, MJ;BATES, DM
通讯作者：
BATES, DM
DOI：
10.1001/jama.1992.03480160073037
发表时间：
1992-04
期刊：
JAMA
影响因子：
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作者：
H. Carter;J. Pearson;E. Metter;L. Brant;L. Brant;Daniel W. Chan;Reubin Andres;Reubin Andres;J. Fozard;P. Walsh
通讯作者：
H. Carter;J. Pearson;E. Metter;L. Brant;L. Brant;Daniel W. Chan;Reubin Andres;Reubin Andres;J. Fozard;P. Walsh