Non-linear mixed effects modeling and simulation (NLME M&S) is evaluated to be used for standardization with longitudinal data in presence of confounders. Standardization is a well-known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that non-linear mixed effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed effects model. Our motivation is that in pharmacometrics NLME M&S is routinely used to analyze clinical trials and to predict and compare potential outcomes of the same patient population under different treatment regimens. Such a comparison is a causal question sometimes referred to as causal prediction. Nonetheless, NLME M&S is rarely positioned as a method for causal prediction. As an example, a simulated clinical trial is used that assumes treatment confounder feedback in which early outcomes can cause deviations from the planned treatment schedule. Being interested in the outcome for the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either using latent conditional exchangeability, conditioning on the individual parameters, or using sequential conditional exchangeability, conditioning on earlier outcomes. Both conditional independencies can be used to estimate the estimand of interest, e.g., with standardization, and they give unbiased estimates.

翻译：非线性混合效应建模与仿真（NLME M&S）被评估用于存在混杂因素的纵向数据标准化。标准化是因果推断中通过分析并合并患者亚组结果来校正混杂因素的经典方法。我们表明，非线性混合效应模型是标准化的一种特定实现，其条件化依赖于混合效应模型中随机效应描述的个体参数。我们的动机在于，药物计量学中常规使用NLME M&S分析临床试验，并预测和比较同一患者群体在不同治疗方案下的潜在结局。这种比较本质上是因果问题，有时被称为因果预测。然而，NLME M&S很少被定位为因果预测方法。以一项模拟临床试验为例，该试验假设存在治疗-混杂反馈机制——早期结局可能导致偏离既定治疗方案。为探究患者依从计划治疗方案的假设情境下的结局，我们将假设纳入因果图中。基于该因果图，可通过两种方式推导条件独立性假设：一是利用潜在条件可交换性（条件化于个体参数），二是利用序贯条件可交换性（条件化于早期结局）。两种条件独立性均可用于估计目标估计量（例如通过标准化方法），并能给出无偏估计。