OBJECTIVES: Indirect treatment comparisons (ITCs) are important when evaluating comparative-effectiveness in absence of head-to-head clinical trials. Classic ITCs can lead to biased results due to differences in patient populations and trial designs. These differences can be corrected for by using matching-adjusted-ITC (MAIC) technique. Furthermore, extrapolation of survival data beyond clinical trial results may be required for economic evaluations. The objective of this research was to compare lenvatinib and sorafenib in patients with RAI-Refractory DTC using MAIC and survival extrapolation techniques. This analysis is an update to the MAIC published previously using a later data cut-off date for both drugs. METHODS: Mean overall-survival (OS) and progression-free survival (PFS) outcomes were estimated by weighting patient-level data based on baseline characteristics from individual phase III trials using logistic regression. Classic ITC was performed before and after adjustment.  Cross-over correction was also applied. Extrapolation of OS and PFS was performed using proportional hazard, accelerated time failure, individual parametric models and piecewise models. Results were presented as hazard-ratios (HR) with confidence-intervals (CI). RESULTS: Unadjusted ITCs for Lenvatinib vs. placebo were 0.545(0.350; 0.830) for OS and 0.213(0.158; 0.288) for PFS. MAIC provided statistically significant estimates of 0.505(0.300; 0.820) for OS and 0.227(0.159; 0.326) for PFS vs. placebo. Unadjusted ITCs vs. sorafenib were 0.790(0.453; 1.379) and 0.361(0.244; 0.534) respectively for OS and PFS; while MAIC results were 0.732(0.396; 1.352) and 0.385(0.248; 0.596) respectively for OS and PFS. Survival extrapolation provided estimates of 8 month of additional OS gain for Lenvatinib vs. sorafenib, with MAIC extrapolation showing largest gain and a good model fit. CONCLUSIONS: This analysis demonstrated that in absence of head-to-head trials, MAIC is an important methodology to adjust for population and trial differences, especially in orphan diseases where limited data are available. MAIC can increase reliability of comparative-effectiveness data and support payers decision making.