Estimating Unknown Transition Times Using a Piecewise Nonlinear Mixed-Effects Model in Men with Prostate Cancer
用分段非线性混合效应模型估计前列腺癌患者的未知转变时间AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要以患者长期重复测量及个体随机效应为核心,直接对应纵向数据方法。
反馈分类问题用分段非线性混合效应模型估计前列腺癌患者的未知转变时间AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
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 including random terms that allow each subject to have his own transition time. The model also accounts for two groups of patients—those with local or regional cancer and those with advanced cancer or whose cancer has metastasized. Various parameters are allowed to differ between these two groups. By backward elimination of statistically nonsignificant parameters, a model is found that adequately describes the data. The model represents a situation where local/regional and advanced/metastatic cancers have similar rates of PSA progression, but advanced/metastatic cancers are diagnosed later. Piecewise mixed-effects models may be useful in a variety of research settings where it is necessary to estimate the unknown time of an event.