Covariate-adjusted Group Sequential Comparisons of Survival Probabilities
协变量调整的生存概率组序贯比较AI 解读 ↓
← 返回论文列表与筛选
阅读说明
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。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
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。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究临床试验中的治疗效应比较与协变量调整，直接对应处理效应推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
协变量调整的生存概率组序贯比较
- 研究问题
- 在可能违反比例风险假设的临床试验中，如何结合协变量调整和期中分析，比较多个固定时点的生存概率？
- 方法与思路
- 作者构造协变量调整的组序贯检验，利用重复统计量的典范联合极限分布及其马尔可夫结构设置临界值和样本量。
- 材料陈述的贡献
- 摘要称该方法支持非比例风险下的边际生存概率比较；模拟达到目标错误率与功效，并用一项移植临床试验说明应用。
- 阅读关注点
- 应核对删失与协变量调整条件、序贯信息量和停止边界，并审查摘要对传统方法在非比例风险下表现的比较依据。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实。摘要未说明删失机制、具体估计形式及模拟覆盖的非比例风险情形。
原文摘要
In confirmatory clinical trials, survival outcomes are frequently studied and interim analyses for efficacy and/or futility are often desirable. Methods such as the log rank test and Cox regression model are commonly used to compare treatments in this setting. They rely on a proportional hazards (PH) assumption and are subject to type I error rate inflation and loss of power when PH are violated. Such violations may be expected a priori, particularly when the mechanisms of treatments differ such as immunotherapy vs. chemotherapy for treating cancer. We develop group sequential tests for comparing survival curves with covariate adjustment that allow for interim analyses in the presence of non-PH and offer easily interpreted, clinically meaningful summary measures of the treatment effect. The joint distribution of repeatedly computed test statistics converges to the canonical joint distribution with a Markov structure. The asymptotic distribution of the test statistics allows marginal comparisons of survival probabilities at multiple fixed time points and facilitates both critical value specification to maintain type I error control and sample size/power determination. Simulations demonstrate that the achieved type I error rate and power of the proposed tests meet targeted levels and are robust to the PH assumption and covariate influence. The proposed tests are illustrated using a clinical trial dataset from the Blood and Marrow Transplant Clinical Trials Network 1101 trial.