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Comparative extinction risk analysis is a common approach for assessing the relative plight of biodiversity and making conservation recommendations. However, the usefulness of such analyses for conservation practice has been questioned. One reason for underperformance may be that threats arising from global environmental changes (e.g., habitat loss, invasive species, climate change) are often overlooked, despite being widely regarded as proximal drivers of species' endangerment. We explore this problem by (i) reviewing the use of threats in this field and (ii) quantitatively investigating the effects of threat exclusion on the interpretation and potential application of extinction risk model results. We show that threat variables are routinely (59%) identified as significant predictors of extinction risk, yet while most studies (78%) include extrinsic factors of some kind (e.g., geographic or bioclimatic information), the majority (63%) do not include threats. Despite low overall usage, studies are increasingly employing threats to explain patterns of extinction risk. However, most continue to employ methods developed for the analysis of heritable traits (e.g., body size, fecundity), which may be poorly suited to the treatment of nonheritable predictors including threats. In our global mammal and continental amphibian extinction risk case studies, omitting threats reduced model predictive performance, but more importantly (i) reduced mechanistic information relevant to management; (ii) resulted in considerable disagreement in species classifications (12% and 5% for amphibians and mammals, respectively, translating to dozens and hundreds of species); and (iii) caused even greater disagreement (20-60%) in a downstream conservation application (species ranking). We conclude that the use of threats in comparative extinction risk analysis is important and increasing but currently in the early stages of development. Priorities for future studies include improving uptake, availability, quality and quantification of threat data, and developing analytical methods that yield more robust, relevant and tangible products for conservation applications.