In randomized controlled trials, ordinal outcomes typically improve statistical efficiency over binary outcomes. The treatment effect on an ordinal outcome is usually described by the odds ratio from a proportional odds model, but this summary measure lacks transparency with respect to its emphasis on the components of the ordinal outcome when proportional odds is violated. We propose various summary measures for ordinal outcomes that are fully transparent in this regard, including 'weighted geometric mean' odds ratios and relative risks, and 'weighted mean' risk differences. We also develop and evaluate efficient model-assisted Bayesian estimators for these population level summary measures based on non-proportional odds models that facilitate covariate adjustment with marginalization via the Bayesian bootstrap. We propose a weighting scheme that engenders appealing invariance properties, including to whether the ordinal outcome is ordered from best to worst versus worst to best. Using computer simulation, we show that comparative testing based on the proposed population level summary measures performs well relative to the conventional proportional odds approach. We also report an analysis of the COVID-OUT trial, which exhibits evidence of non-proportional odds.

翻译：在随机对照试验中，序数结局通常比二分类结局具有更高的统计效率。对于序数结局的治疗效果，通常通过比例优势模型中的比值比来描述，但当比例优势假设不成立时，该汇总指标在强调序数结局各组成部分方面缺乏透明度。我们为此提出了多种完全透明的序数结局汇总指标，包括“加权几何平均”比值比和相对风险，以及“加权平均”风险差。我们还基于非比例优势模型，开发并评估了针对这些总体水平汇总指标的高效模型辅助贝叶斯估计器，这些模型通过贝叶斯自助法进行边际化处理，便于协变量调整。我们提出了一种加权方案，该方案具有吸引人的不变性特性，包括对序数结局是从最佳到最差排序还是从最差到最佳排序的不变性。通过计算机模拟，我们表明，基于所提出的总体水平汇总指标的比较性检验相对于传统的比例优势方法表现良好。我们还报告了对COVID-OUT试验的分析，该试验显示出非比例优势的证据。