OBJECTIVES :

The natural disease course of relapsed or refractory peripheral T-cell lymphoma(R/R PTCL) is not simple and its drug regimen is complicated. Therefore, we developed a time-dependent Markov model with nested states reflecting Korean treatment pattern to aid a valid cost-effectiveness assessment of treatment options for R/R PTCL in the future study.

METHODS

We conducted a literature review and clinician panel survey to investigate health states, treatment patterns, and characteristics of Korean R/R PTCL patients. The treatment schedule was estimated based on the dosing schedule and proportion of patients receiving conventional chemotherapy using Korean data.

RESULTS :

Five health states were identified in a Markov model with a weekly cycle. Simulation starts at ‘initial treatment’ state. ‘Treatment pause’ state lasts for 8 weeks after initial or subsequent treatment state. Patients achieving complete response(CR) or not eligible to undergo stem cell transplantation(SCT) due to comorbidity, remain in the ‘treatment pause’. ‘Subsequent treatment’ state is the state where patients in partial response(PR), stable disease(SD), or progressive disease(PD) move to. Patients achieving CR or PR in the treatment pause state can undergo SCT after 20 weeks of starting initial or subsequent therapy applying transition probability, and can move to ‘SCT success’ state if SCT is achieved. Once in SCT success, we assume no relapse after SCT and patients stay in that state for their whole life based on a clinician survey. ‘Death’ is an absorbing state. Except for ‘SCT success’ and ‘death’ state, three health states are composed of nested states(CR, PR, SD, or PD) depending on the response rate of each treatment arm. Transition probabilities to each nested state and to SCT success state varies by the time spent after the model simulation starts.

CONCLUSIONS

We expect our study demonstrates how an economic evaluation model can be developed by reflecting natural disease course and real-world practice patterns.