基于参考的重复计数结果敏感性分析：使用负二项边缘分布与高斯copula
Reference-based sensitivity analysis for repeated count outcomes using negative binomial margins and a Gaussian copula
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- Cytel Inc.（Cytel公司）
- University of the Free State（自由州大学）
- KU Leuven（荷语鲁汶大学）
- Stellenbosch University（斯泰伦博斯大学）
- Utrecht University（乌得勒支大学）
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中文总结 AI 辅助
本研究提出基于高斯copula和负二项边缘分布的参考-based多重插补方法，用于纵向计数结果，通过模拟和实例验证了其在敏感性分析中的有效性。
中文摘要 AI 辅助
参考-based多重插补用于纵向临床试验中，评估在发生间发事件后未观测结果假设的敏感性。现有大多数方法针对连续结果，并使用多元正态工作模型。计划内的计数结果需要一种模型，该模型保留整数支持、偏度、过度离散、纵向依赖性以及基于暴露的率解释。我们提出由高斯copula连接的访视特异性负二项（NB）边缘分布。协变量调整的对数率模型定义边缘分布，而copula捕捉纵向依赖性。对于预先指定事件（称为触发）后活性组中缺失的结果，分配组继续保留拟合的活性组边缘均值，跳转到参考组使用相应的参考组边缘均值，中间规则在对数率尺度上在这些均值之间插值。结合copula，所得的NB边缘分布确定基于保留历史的插补分布。我们通过随机化概率积分变换增广来处理离散性，并使用自定义的Metropolis-within-Gibbs采样器拟合模型。在定向模拟中，模型以较小偏差恢复了生成边缘和依赖参数。在不完整数据的模拟中，当插补规则与触发后结果生成机制匹配时，治疗效果估计最接近相应的完整数据估计。我们使用已发表的在膀胱过度活动症试验中的重复尿失禁发作次数来演示该方法。在所有参考-based假设下，比较活性治疗与安慰剂的估计率比保持在1以下，向零值适度衰减，且在最后一次访视时各规则之间的分离最大。
英文摘要
Reference-based multiple imputation is used in longitudinal clinical trials to assess sensitivity to assumptions about outcomes unobserved after intercurrent events. Most existing methods target continuous outcomes and use multivariate normal working models. Scheduled count outcomes require a model that preserves integer support, skewness, overdispersion, longitudinal dependence, and an exposure-based rate interpretation. We propose visit-specific negative binomial (NB) margins linked by a Gaussian copula. Covariate-adjusted log rate models define the margins, while the copula captures longitudinal dependence. For missing outcomes in the active arm after a prespecified event, termed the trigger, assigned arm continuation retains the fitted active arm marginal mean, jump to reference uses the corresponding reference arm marginal mean, and the intermediate rule interpolates between these means on the log rate scale. Combined with the copula, the resulting NB margins determine an imputation distribution conditional on the retained history. We account for discreteness through randomized probability integral transform augmentation and fit the model with a custom Metropolis-within-Gibbs sampler. In targeted simulations, the model recovered the generating marginal and dependence parameters with little bias. In simulations with incomplete data, treatment effect estimates were closest to the corresponding complete data estimates when the imputation rule matched the mechanism governing outcomes after the trigger. We illustrate the method using repeated incontinence episode counts from a published trial in overactive bladder. Estimated rate ratios comparing active treatment with placebo remained below 1 under all reference-based assumptions, with modest attenuation toward the null and the greatest separation between rules at the final visit.
