In a Markov model, a cohort of patients moves from one health state to another at defined times depending on the assigned transition probabilities. We used time-dependent transition probabilities for the Markov model derived from cumulative survival distributions, meaning that the transition probabilities change as the time in a health state increases. This is discussed in more details in the section on model parameters.

The Markov model developed for transplanting a low-quality kidney (KDPI > 74) had three health states: transplantation, return to dialysis post kidney transplantation failure and death (Fig. (Fig.1).1). The cohort starts at “KDPI > 74 kidney transplant” health state and tracks the outcomes of: return to dialysis post kidney transplantation failure, death, or successfully functioning transplant. In the event of graft failure, the patient can have subsequent outcomes of either remain on dialysis or die while on dialysis. It was assumed that no patients had a re-transplantation following graft failure. The Markov model for the cohort remaining waitlisted for a kidney had two health states: die while on dialysis or remain healthy (Fig. (Fig.11).

We fitted four Markov models (Table (Table1)1) with cycles of 6 or 12 months, each with two-time horizons (5 and 20 years). Different time-horizons were used to assess how the main outputs change over time, while different cycle lengths were used to assess whether Markov models with shorter cycle length would more closely correspond with the DES model results. A half-cycle correction was used for all the models.

Markov models fitted in the study

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