具有竞争风险的治愈率数据的缺陷回归模型。

IF 1.2 4区医学 Q4 PHARMACOLOGY & PHARMACY

Journal of Biopharmaceutical Statistics Pub Date : 2024-11-14 DOI:10.1080/10543406.2024.2424838

K Silpa, E P Sreedevi, P G Sankaran

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引用次数: 0

摘要

本文提出了一种利用缺陷分布分析具有竞争风险的治愈率数据的新方法。我们建立了两个缺陷回归模型，用于分析随机右删减的竞争风险数据。通过所建模型，我们可以直接从模型中估算出治愈率。同时，我们还使用最大似然法估算了与每个故障原因相对应的回归参数。我们进行了一项模拟研究，以评估所提出的估计器的有限样本性能。我们使用两个真实数据集说明了这些程序的实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Defective regression models for cure rate data with competing risks.

In this paper, we propose a novel method for the analysis of cure rate data with competing risks using defective distributions. We develop two defective regression models for the analysis of competing risk data subjected to random right censoring. The proposed models enable us to estimate the cure fraction directly from the model. Simultaneously, we also estimate the regression parameters corresponding to each cause of failure using the method of maximum likelihood. We conduct a simulation study to evaluate the finite sample performance of the proposed estimators. The practical usefulness of the procedures is illustrated using two real-life data sets.

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来源期刊

Journal of Biopharmaceutical Statistics 医学-统计学与概率论

CiteScore

2.50

自引率

18.20%

发文量

审稿时长

6-12 weeks

期刊介绍： The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.