she would not have been eligible for the study, judgement is required. The study result probably applies even if, for example, she was 2 years too old for the study, had more severe disease, had previously been treated with a competing therapy, or had a comorbid condition. A better approach than rigidly applying the study's inclusion and exclusion criteria is to ask whether there is some compelling reason why the results should not be applied to the patient. A compelling reason usually won't be found, and most often you can generalize the results to your patient with confidence. A final issue arises when our patient fits the features of a subgroup of pateints in the trial report. In articles reporting the results of a trial (especially when the treatment doesn't appear to be efficacious for the average patient), the authors may have examined a large number of subgroups of patients at different stages of their illness, with different comorbid conditions, with different ages at entry, and the like. Quite often these subgroup analyses were not planned ahead of time, and the data are simply "dredged" to see what might turn up. Investigators may sometimes over-interpret these "data-dependent" analyses as demonstrating that the treatment really has a different effect in a subgroup of patients -- those who are older or sicker, for instance, may be held up as benefitting substantially more or less than other subgroups of patients in the trial. You can find guides for deciding whether to believe these