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Analyzing a Randomized Cancer Prevention Trial with a Missing Binary Outcome, an Auxiliary Variable, and All-or-None Compliance

含缺失二元结局、辅助变量与全有或全无依从性的随机癌症预防试验分析

The Prostate Cancer Prevention Trial is a randomized chemoprevention trial designed to compare the effect of daily finasteride versus placebo on prostate cancer determined by biopsy. Investigators have scheduled a biopsy at the end of the trial in 7 years or following a positive prostate-specific antigen (PSA) on annual screening. The analysis will need to adjust for two likely complications. First, some subjects will not receive a biopsy, depending in part on whether or not…

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Analyzing a Randomized Cancer Prevention Trial with a Missing Binary Outcome, an Auxiliary Variable, and All-or-None Compliance

结局缺失、辅助变量及全有或全无依从下的随机癌症预防试验分析

The Prostate Cancer Prevention Trial is a randomized chemoprevention trial designed to compare the effect of daily finasteride versus placebo on prostate cancer determined by biopsy. Investigators have scheduled a biopsy at the end of the trial in 7 years or following a positive prostate-specific antigen (PSA) on annual screening. The analysis will need to adjust for two likely complications. First, some subjects will not receive a biopsy, depending in part on whether or not…

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Analysis of Survival Data from a Randomized Trial with All-or-None Compliance: Estimating the Cost-Effectiveness of a Cancer Screening Program

完全接受或完全拒绝依从下的随机试验生存分析:癌症筛查成本效果估计

Many randomized cancer screening trials involve all-or-none compliance. Some subjects randomized to an offer of screening refuse screening, and some subjects randomized to no offer of screening obtain screening outside the trial. The primary analysis to test whether or not cancer screening reduces cancer mortality is by intent-to-treat. To estimate the cost-effectiveness of screening, it is necessary to adjust for all-or-none compliance. Heretofore, adjustments for…

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Regression Analysis for Categorical Variables with Outcome Subject to Nonignorable Nonresponse

存在不可忽略无应答的分类结果回归分析

We develop a log-linear model for categorical response subject to nonignorable nonresponse. The paper differs from Fay (1986) in its focus on estimation and hypothesis testing in a regression setting, as opposed to imputation in a multivariate setting. We present several new results concerning the existence of solutions on the boundary of the parameter space and the construction of confidence intervals for estimates. We illustrate the method by estimating the proportion of…

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