Multi-drug-resistant tuberculosis (MDR-TB) is defined as strains of tuberculosis that are resistant to at least the two most powerful anti-TB drugs. It is often treated with multiple drugs. Our data consist of individual patient data from 31 international observational studies. We develop identifiability criteria for the estimation of a generalized treatment importance metric in the context where not all medications are observed in all studies. We then use this metric to rank 15 observed antibiotics. Using the concept of transportability, we propose an implementation of targeted maximum likelihood estimation (TMLE), a doubly robust and locally efficient plug-in estimator, to estimate the treatment importance metric. A clustered sandwich estimator is adopted to compute variance estimates. Simulation studies are conducted to assess the performance of our estimator, verify the double robustness property, and assess the appropriateness of the variance estimation approach.