In target trial emulation (TTE), marginal structural models (MSMs) can be used to characterise per-protocol treatment effects over time. The MSM parameters are often estimated by inverse probability weighting (IPW), with weights estimated by maximum likelihood. However, IPW-based estimators can be unstable in small samples and are sensitive to misspecification of the weight models. An alternative method for estimating the MSM parameters is longitudinal targeted maximum likelihood estimation (LTMLE). LTMLE is double robust and potentially more efficient than IPW. Nevertheless, LTMLE also relies on inverse probability weights and may therefore share the instability of IPW-based estimators. We propose joint calibrated LTMLE, which integrates LTMLE with joint calibrated weights tailored for per-protocol effect estimation in TTE. This calibration of weights improves finite-sample performance by enforcing covariate balance in both the treatment and censoring processes simultaneously. Simulations show that the proposed method has improved efficiency and robustness to weight model misspecification, compared to standard LTMLE. We illustrate the method using a case study to evaluate the effect of highly active antiretroviral therapy on CD4 cell count among HIV-positive women.

翻译：在靶试验模拟中，边际结构模型可用于刻画随时间变化的按方案治疗效果。MSM参数通常通过逆概率加权估计，其权重由最大似然法获得。然而，基于IPW的估计量在小样本中可能不稳定，且对权重模型的误设敏感。估计MSM参数的替代方法是纵向靶最大似然估计。LTMLE具有双重稳健性，且可能比IPW更高效。但LTMLE同样依赖于逆概率权重，因此可能具有与IPW估计量相似的不稳定性。我们提出联合校准LTMLE，该方法将LTMLE与专为TTE中按方案效应估计设计的联合校准权重相结合。这种权重校准通过同时强制处理与删失过程中的协变量平衡，改善了有限样本性能。模拟显示，与标准LTMLE相比，所提方法在权重模型误设时具有更高的效率与稳健性。我们通过评估高效抗逆转录病毒疗法对HIV阳性女性CD4细胞计数影响的案例研究，对该方法进行了说明。