Does Finasteride Affect the Severity of Prostate Cancer? A Causal Sensitivity Analysis

• 成果类型: Article
• 署名作者: Shepherd, Bryan E.; Redman, Mary W.; Ankerst, Donna R.
• 署名单位: Vanderbilt University; Technical University of Munich
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000706
• 发表日期: 2008
• 页码: 1392-1404
• 关键词: semiparametric regression; principal stratification; repeated outcomes; prevention trial; VIRAL LOAD; inference; psa

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 oil 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 7 years. Enthusiasm for the risk reduction afforded by the chemopreventative agent and adoption Of its use in clinical practice. however. was severely dampened by (lie additional finding in the trial of an increased absolute number of high-grade (Gleason score >= 7) cancers oil 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 postrandomization election 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 dropout. 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 postrandomization biases conjectured in the PCPT.