Cro Suzie, Quartagno Matteo, White Ian R, Carpenter James R
Imperial Clinical Trials Unit, Imperial College London, London, UK.
MRC Clinical Trials Unit at UCL, University College London, London, UK.
Stat Med. 2025 Feb 10;44(3-4):e10301. doi: 10.1002/sim.10301.
In clinical trials, a treatment policy strategy is often used to handle treatment nonadherence. However, estimation in this context is complicated when data are missing after treatment deviation. Reference-based multiple imputation has been developed for the analysis of a longitudinal continuous outcome in this setting. It has been shown that Rubin's variance estimator ensures that the proportional loss of information due to missing data is approximately the same as that seen in analysis under the missing-at-random assumption for a broad range of commonly used reference-based alternatives; that is it is information anchored. However, the best way to implement reference-based multiple imputation for longitudinal binary data is unclear.
We formulate and describe two algorithms for implementing reference-based multiple imputation for longitudinal binary outcome data using: (i) joint modeling with the multivariate normal distribution and an adaptive rounding algorithm and (ii) joint modeling with a latent multivariate normal model. A simulation study was performed to compare the properties of the two methods.
Across the broad range of scenarios evaluated, the latent normal approach typically gave slightly less bias; both methods provided approximately information anchored inference. The advantage of the latent normal approach was more marked with a rarer outcome. However, both approaches may not perform satisfactorily if the outcome prevalence is very rare, that is, .
Reference-based multiple imputation provides a practical information anchored tool for inferences about the treatment effect for a treatment policy estimand with a longitudinal binary outcome. The latent multivariate normal model is the preferred implementation.
在临床试验中，常采用治疗策略来处理治疗不依从问题。然而，当治疗出现偏差后数据缺失时，这种情况下的估计会变得复杂。基于参考的多重填补方法已被开发用于分析这种情况下的纵向连续结局。研究表明，鲁宾方差估计器可确保在广泛的常用基于参考的替代方法下，因数据缺失导致的信息比例损失与在随机缺失假设下分析时所见的损失大致相同；也就是说它是信息锚定的。然而，对于纵向二元数据实施基于参考的多重填补的最佳方法尚不清楚。
我们制定并描述了两种用于对纵向二元结局数据实施基于参考的多重填补的算法，使用：（i）与多元正态分布的联合建模及自适应舍入算法，以及（ii）与潜在多元正态模型的联合建模。进行了一项模拟研究以比较这两种方法的性质。
在评估的广泛场景中，潜在正态方法通常偏差略小；两种方法都提供了大致信息锚定的推断。潜在正态方法的优势在结局较罕见时更为明显。然而，如果结局患病率非常低，即 ，两种方法可能都不能令人满意地发挥作用。
基于参考的多重填补为推断具有纵向二元结局的治疗策略估计量的治疗效果提供了一种实用的信息锚定工具。潜在多元正态模型是首选的实施方法。
