Introduction. There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of non-informative and informative censoring through a simulation. Methods. We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. Results. The pseudo-value, BLR-IPCW and MLR-IPCW approaches give unbiased estimates of the calibration curves under non-informative censoring. These methods remained unbiased in the presence of informative censoring, unless the mechanism was strongly informative, with bias concentrated in the areas of predicted transition probabilities of low density. Conclusions. We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot, which provides additional information over either of the other methods.

翻译：引言。目前尚无指导如何评估用于风险预测的多状态模型校准的方法。我们引入了几种可用于生成多状态模型转移概率校准图的技术，并通过模拟在存在非信息性和信息性删失的情况下评估其性能。方法。我们研究了基于Aalen-Johansen估计量的伪值法、带有逆删失概率加权的二元逻辑回归（BLR-IPCW）以及带有逆删失概率加权的多项逻辑回归（MLR-IPCW）。MLR-IPCW方法生成校准散点图，提供关于校准的额外洞察。我们模拟了具有不同删失水平的数据，并评估了每种方法估计一组预测转移概率校准曲线的能力。我们还开发并评估了一个模型，该模型预测由关联初级和二级医疗记录得出的患者队列中心血管疾病、2型糖尿病和慢性肾病的发病率。结果。在非信息性删失下，伪值法、BLR-IPCW和MLR-IPCW方法给出了校准曲线的无偏估计。这些方法在信息性删失下仍然保持无偏性，除非删失机制具有强信息性，且偏差集中在预测转移概率的低密度区域。结论。我们建议采用伪值法或BLR-IPCW方法生成校准曲线，并结合MLR-IPCW方法生成校准散点图，后者相比其他两种方法能提供额外信息。