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. There is currently no guidance on how to assess the calibrationof multistate models used for risk prediction. We introduce several techniquesthat can be used to produce calibration plots for the transition probabilitiesof a multistate model, before assessing their performance in the presence ofnon-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 ofcensoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibrationscatter plot, providing extra insight about the calibration. We simulated datawith varying levels of censoring and evaluated the ability of each method toestimate the calibration curve for a set of predicted transition probabilities.We also developed evaluated the calibration of a model predicting the incidenceof cardiovascular disease, type 2 diabetes and chronic kidney disease among acohort of patients derived from linked primary and secondary healthcarerecords.  Results. The pseudo-value, BLR-IPCW and MLR-IPCW approaches give unbiasedestimates of the calibration curves under non-informative censoring. Thesemethods remained unbiased in the presence of informative censoring, unless themechanism was strongly informative, with bias concentrated in the areas ofpredicted transition probabilities of low density.  Conclusions. We recommend implementing either the pseudo-value or BLR-IPCWapproaches to produce a calibration curve, combined with the MLR-IPCW approachto produce a calibration scatter plot, which provides additional informationover either of the other methods.

Calibration plots for multistate risk predictions models: an overview and simulation comparing novel approaches