Markov Cohort Trace Explorer

Change the transition probabilities, cycle count, state costs and utilities to see how a cohort moves through Stable, Progressed and Death states over time.

Model inputs

Each transition-matrix row describes where people in the starting state go during one cycle. Every row must sum to 1.

Starting cohort

Number of cycles

Cost discount rate (%)

QALY discount rate (%)

Transition matrix

From \ To	Stable	Progressed	Death	Row sum

Probabilities must be between 0 and 1. Death is an absorbing state in this example.

State rewards per cycle

State	Cost per person	Utility weight

Teaching model: State occupancy is valued at the start of each cycle. This simplified example does not apply a half-cycle correction or transition rewards.

Cohort trace and outcomes

Discounted cost units—

Discounted QALYs—

Alive after final cycle—

Equivalent data table

Cycle	Stable	Progressed	Death	Cycle cost	Cycle QALYs

How the model works

The cohort starts in the Stable state. For each cycle, the current state vector is multiplied by the transition matrix: s(t+1) = s(t)P. Because the transition probabilities are constant, this example applies the same matrix in every cycle and reflects the Markov memory assumption.

Costs and QALYs are calculated from the number of people occupying each state. Cycle t is discounted by 1 / (1 + r)^t. The model is illustrative and is not intended for clinical or policy decisions.