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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 random and independent censoring through a simulation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We studied pseudo\u2010values based on the Aalen\u2010Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR\u2010IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR\u2010IPCW). The MLR\u2010IPCW 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  and 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The pseudo\u2010value, BLR\u2010IPCW, and MLR\u2010IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low\u2010density regions of predicted transition probabilities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We recommend implementing either the pseudo\u2010value or BLR\u2010IPCW approaches to produce a calibration curve, combined with the MLR\u2010IPCW approach to produce a calibration scatter plot. 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