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AI 根据所提供的论文材料归类:摘要明确采用主层因果目标、逆概率加权和随机化后偏差敏感性分析,直接属于因果推断。

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代码、数据与补充材料

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关注这篇论文的作者
Bryan E. Shepherd
Mary W. Redman
Donna P. Ankerst

非那雄胺是否影响前列腺癌严重程度:因果敏感性分析

研究问题
随机试验中非那雄胺组高级别癌病例较多,是否反映药物提高癌症严重程度,还是源于随机分组后的选择与检测偏差?
方法与思路
作者用逆概率加权估计方程,估计无论分配何种治疗都会患癌者这一主层中的平均严重程度差异,并逐步开展敏感性分析。
材料陈述的贡献
摘要称,分析围绕明确的主层因果目标,依次处理病例构成、活检验证、分级差异及不可忽略退出等潜在随机化后偏差。
阅读关注点
应核对主层目标的定义与识别条件、权重构造、敏感性参数,以及各类偏差调整是否改变对严重程度效应的判断。
阅读边界
所据为 OpenAlex 索引摘要,稿件版本未注明;摘要未报告最终效应、区间或敏感性结果,不能据此判断药物是否加重癌症。

原文摘要

In 2003 Thompson and colleagues reported that daily use of finasteride reduced the prevalence of prostate cancer by 25% compared to placebo. These results were based on the double-blind randomized Prostate Cancer Prevention Trial (PCPT) which followed 18,882 men with no prior or current indications of prostate cancer annually for seven years. Enthusiasm for the risk reduction afforded by the chemopreventative agent and adoption of its use in clinical practice, however, was severely dampened by the additional finding in the trial of an increased absolute number of high-grade (Gleason score >/= 7) cancers on the finasteride arm. The question arose as to whether this finding truly implied that finasteride increased the risk of more severe prostate cancer or was a study artifact due to a series of possible post-randomization selection biases, including differences among treatment arms in patient characteristics of cancer cases, differences in biopsy verification of cancer status due to increased sensitivity of prostate-specific antigen under finasteride, differential grading by biopsy due to prostate volume reduction by finasteride, and nonignorable drop-out. Via a causal inference approach implementing inverse probability weighted estimating equations, this analysis addresses the question of whether finasteride caused more severe prostate cancer by estimating the mean treatment difference in prostate cancer severity between finasteride and placebo for the principal stratum of participants who would have developed prostate cancer regardless of treatment assignment. We perform sensitivity analyses that sequentially adjust for the numerous potential post-randomization biases conjectured in the PCPT.

笔记与分组
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