Department of Statistics, North Carolina State University, Raleigh, North Carolina.
Biometrics. 2023 Jun;79(2):975-987. doi: 10.1111/biom.13603. Epub 2021 Dec 17.
In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (OR; active agent vs control) under the assumption of a proportional odds model. Although at the final analysis the outcome will be determined for all subjects, at an interim analysis, the status of some participants may not yet be determined, for example, because ascertainment of the outcome may not be possible until some prespecified follow-up time. Accordingly, the outcome from these subjects can be viewed as censored. A valid interim analysis can be based on data only from those subjects with full follow-up; however, this approach is inefficient, as it does not exploit additional information that may be available on those for whom the outcome is not yet available at the time of the interim analysis. Appealing to the theory of semiparametrics, we propose an estimator for the OR in a proportional odds model with censored, time-lagged categorical outcome that incorporates additional baseline and time-dependent covariate information and demonstrate that it can result in considerable gains in efficiency relative to simpler approaches. A byproduct of the approach is a covariate-adjusted estimator for the OR based on the full data that would be available at a final analysis.
在许多针对 COVID-19 治疗的随机临床试验中，主要结局是有序分类变量，并且关注点集中在比例优势模型假设下的优势比（OR；活性药物与对照）。虽然最终分析将确定所有受试者的结局，但在中期分析时，一些参与者的情况可能尚未确定，例如，因为直到规定的随访时间才能确定结局。因此，这些受试者的结局可以视为截尾。基于仅来自那些具有完整随访的受试者的数据，可以进行有效的中期分析；然而，这种方法效率低下，因为它没有利用在中期分析时对于那些结局尚未可用的受试者可能可用的其他信息。根据半参数理论，我们提出了一种带有截尾、时滞分类结局的比例优势模型中的 OR 估计量，该估计量纳入了额外的基线和时变协变量信息，并证明它相对于更简单的方法可以带来相当大的效率增益。该方法的一个副产品是基于最终分析时可用的完整数据的基于协变量调整的 OR 估计量。
