Randomized clinical trials typically aim to estimate a marginal treatment effect. While covariate adjustment can improve precision, it may change the estimand in nonlinear models due to noncollapsibility, leading to conditional rather than marginal treatment effects. At the same time, identifying prognostic and predictive covariates is important for understanding treatment effect heterogeneity and informing clinical decision-making. Keeping marginal interpretability while allowing efficiency gains and assessment of heterogeneity remains a methodological challenge. In this work, we extend nonparanormal adjusted marginal inference to allow for heterogeneous treatment effects. The proposed framework embeds the marginal treatment effect directly in a joint model for the outcome and baseline covariates. This construction preserves marginal interpretability while adjusting for potentially prognostic and/or predictive covariates. The method applies to continuous, binary, ordinal, and time-to-event outcomes and allows explicit estimation and ranking of prognostic and predictive covariates on a common scale. For continuous outcomes, we show that the asymptotic variance of the marginal treatment effect measured as Cohen's $d$ is never worse and often better under covariate adjustment than without adjustment. Efficiency gains are primarily driven by prognostic effects, with realistic predictive effects contributing little additional improvement. Simulation studies confirm these findings across outcome types and demonstrate unbiased and more efficient estimation of marginal effects for Cohen's d, log-odds ratios, and log-hazard ratios. Application to an acupuncture trial demonstrates that the method reproduces the original trial findings while improving efficiency and allowing ranking of prognostic and predictive covariates.

翻译：随机临床试验通常旨在估计边际处理效应。虽然协变量调整能提高估计精度，但在非线性模型中，由于非可压缩性，可能导致估计目标从边际处理效应转为条件处理效应。同时，识别预后性和预测性协变量对理解处理效应异质性和指导临床决策至关重要。如何在保持边际效应可解释性的同时实现效率提升与异质性评估仍是方法论难题。本研究将非参数正态调整边际推断扩展至异质性处理效应情境。所提出的框架将边际处理效应直接嵌入结果变量与基线协变量的联合模型中。这种构造在调整潜在预后性和/或预测性协变量的同时保留了边际效应的可解释性。该方法适用于连续型、二分类、有序型及时间事件结局，并能在统一尺度下对预后性协变量和预测性协变量进行显式估计与排序。对于连续型结局，我们证明在采用Cohen's d量化的边际处理效应中，协变量调整后的渐近方差始终不劣于且通常优于未调整情形。效率增益主要源于预后效应，而实际预测效应带来的额外改善甚微。模拟研究验证了这些发现对不同结局类型的普适性，并展示了在Cohen's d、对数优势比和对数风险比条件下对边际效应的无偏且更高效估计。将本方法应用于针刺试验的实证分析表明，该方法在提升效率的同时复现了原始试验结论，并实现了预后性协变量与预测性协变量的排序。