In most therapeutic areas, multiple drug options are increasingly becoming available, but there is often a lack of evidence from head‐to‐head clinical trials that allows for direct comparison of the efficacy and/or safety of one drug vs. another. This review provides an introduction to, and overview of, common methods used for comparing drugs in the absence of head‐to‐head clinical trial evidence. Naïve direct comparisons are in most instances inappropriate and should only be used for exploratory purposes and when no other options are possible. Adjusted indirect comparisons are currently the most commonly accepted method and use links through one or more common comparators. Mixed treatment comparisons (MTCs) use Bayesian statistical models to incorporate all available data for a drug, even data that are not relevant to the comparator drug. MTCs reduce uncertainty but have not yet been widely accepted by researchers, nor drug regulatory and reimbursement authorities. All indirect analyses are based on the same underlying assumption as meta‐analyses, namely that the study populations in the trials being compared are similar.

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In the current climate of burgeoning health care costs, pharmacoeconomics is becoming increasingly important, but knowledge about pharmacoeconomic methods is limited among most clinicians. This review provides an introduction to, and overview of, common methods used in pharmacoeconomic modelling: decision analysis, Markov modelling, discounting and uncertainty analyses via Monte Carlo simulation. It will conclude with a suggested approach to reading and appraising published pharmacoeconomic analyses.