医疗保健提供者对患者结局因果影响的多水平潜在类（MLC）建模：通过模拟进行评价
Multilevel latent class (MLC) modelling of healthcare provider causal effects on patient outcomes: Evaluation via simulation
摘要：
如果需要对医疗保健提供者的绩效进行比较，则必须在提供者之间平衡患者的特征和感兴趣的健康状况，以便进行公平比较。这在观察性数据中不太可能可行，因为患者人群特征可能因地理位置而异，患者护理可能因健康状况特征而异。我们基于先前使用的真实世界数据集模拟了患者和提供者的数据，并在较高水平上分别考虑了二元和连续协变量效应。多层潜在类（MLC）建模提出了分区的预测重点在病人的水平（容纳casemix）和因果推断重点在供应商的水平。MLC模型恢复了一系列模拟的信任水平效应。中位恢复值几乎与二进制信任水平协变量的模拟值相同，并且我们观察到至少有3个潜在信任类的连续信任水平协变量的成功恢复。可信区间随着误差方差的增加而变宽。MLC方法成功地划分了预测和因果推断的建模，解决了这两种不同分析策略之间的潜在冲突。这改进了仅针对差异选择进行调整的策略。患者水平的变化和测量的不确定性被容纳在潜在的类。
英文摘要：
Where performance comparison of healthcare providers is of interest, characteristics of both patients and the health condition of interest must be balanced across providers for a fair comparison. This is unlikely to be feasible within observational data, as patient population characteristics may vary geographically and patient care may vary by characteristics of the health condition. We simulated data for patients and providers, based on a previously utilized real-world dataset, and separately considered both binary and continuous covariate-effects at the upper level. Multilevel latent class (MLC) modelling is proposed to partition a prediction focus at the patient level (accommodating casemix) and a causal inference focus at the provider level. The MLC model recovered a range of simulated Trust-level effects. Median recovered values were almost identical to simulated values for the binary Trust-level covariate, and we observed successful recovery of the continuous Trust-level covariate with at least 3 latent Trust classes. Credible intervals widen as the error variance increases. The MLC approach successfully partitioned modelling for prediction and for causal inference, addressing the potential conflict between these two distinct analytical strategies. This improves upon strategies which only adjust for differential selection. Patient-level variation and measurement uncertainty are accommodated within the latent classes.
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