OBJECTIVES: Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for researchers to know about the steps in selecting and using the most suitable ones. This paper is aimed at proposing a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data. The use of the proposed tutorial is illustrated based on the final progression-free survival (PFS) analysis of BOLERO-2 trial in metastatic breast cancer (mBC). METHODS: An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct individual patient data (IPD), based on the final PFS analysis of BOLERO-2 trial. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions. RESULTS: The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published KM curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial. CONCLUSIONS: In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. These results can serve as a basis for any model (Markov) that needs the parameterization of transition probabilities, and only has summary Kaplan-Meier plots available.