Abstract
Purpose
(1)To give an overview on published decision-analytic models evaluating treatment strategies in multiple myeloma with a focus on methodological aspects of modeling approaches. (2) To derive recommendations for future decision-analytic models analyzing different treatment regimens for multiple myeloma patients.
Methods
A systematic literature search was performed in the electronic databases Pubmed, NHS EED and the Tufts CEA Registry to identify studies evaluating multiple myeloma treatment strategies using mathematical decision-analytic models. To meet the inclusion criteria, models were required to compare different treatment strategies, to be published as full text articles in English, and comprise relevant clinical health outcomes (e.g., responses, progression-free survival, or QALYs) over a defined time horizon and population. Evaluation of costs was optional. We used evidence tables to summarize methodological characteristics, such as modeling approach and validation, simulation technique, health outcomes evaluated, included states/events, perspective, time horizon and uncertainty analysis.
Results
We found eleven decision-analytic modeling studies. Economic evaluations were included in all studies. The modeling approaches applied included a decision tree model, Markov cohort model, discrete event simulations, partitioned survival analyses and area under the curve models. Time horizons ranged from seven years to lifetime. Six models adopted the perspective of the health care system, three a third party payer, two the government payer and only one the societal perspective. Health outcomes included (overall, median, progression-free) survival, number needed to treat, time to discontinuation of treatment, life expectancy, and QALYs. Evaluated treatment strategies included lenalidomide, dexamethasone, bortezomib, melphalan, prednisone, thalidomide, haemodialysis, bone marrow transplantation, zoledronic acid, and clodronate. Health states used in the models were mainly either not progressed, progressed and death, or some authors also took into account the hematological response to therapy (partial, complete). In most studies, model validation was only mentioned in the discussion when comparing the results with other cost-effectiveness studies. All authors performed deterministic sensitivity analyses. Additionally, seven articles reported a probabilistic sensitivity analysis.
Conclusions
We identified several well-designed models for different multiple myeloma treatment strategies evaluating relevant health outcomes as well as economic parameters. However, the quality of reporting varied considerably and in some cases the models were not sufficiently described. For the future, we recommend an explicit model description including all relevant parameters and model validation using independent data.
This work was supported by the COMET Center ONCOTYROL, which is funded by the Austrian Federal Ministries BMVIT/BMWFJ (via FFG) and the Tiroler Zukunftsstiftung/Standortagentur Tirol (SAT).
Disclosures:
No relevant conflicts of interest to declare.
Combining value of information analysis and ethical argumentation in decisions on participation of vulnerable patients in clinical research
