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  "path": "/t/generalizability-vs-transportability-in-trials/28551?page=3#post_50",
  "publishedAt": "2026-03-01T12:35:45.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "Pavlos_Msaouel:\n\n> The fundamental question was not “Is my population the same as the trial population?” but rather “Is there a compelling reason why the results of this trial should not apply to _my individual patient_?”\n\nI do not find Sackett’s formulation particularly illuminating. A randomized trial estimates an average treatment effect, not the effect for any specific patient. This is true whether or not an individual patient participated in the trial. The relevant question is not trial participation, but whether the causal mechanism tested in the trial is operative in the patient under consideration.\n\nConsider a trial of streptomycin for alveolar tuberculosis. A patient with a streptomycin-resistant strain may have been enrolled in the trial or not; what matters is resistance, because resistance removes the causal pathway by which streptomycin exerts benefit. Conversely, a physician may reasonably generalize results to chronic cavitary TB if the same antimicrobial mechanism is plausibly operative. Causal coherence, not cohort membership, is the decisive issue.\n\nThis is the modern pinch point. Care is increasingly protocol-driven rather than physician-specific, so trial results are rapidly generalized to entire populations. Generalization now sits high on the patient-risk hierarchy, and individual clinicians may not be in a position to protect patients from false transport.\n\nA recent example illustrates the danger. Evidence-based guidelines recommended “high PEEP” ventilator strategies for severe COVID-19 pneumonia, “extrapolated” (generalized) from ARDS trials in which COVID pneumonia did not exist. These dangerous protocol-level generalizations were later abandoned, yet the methodological lesson was largely ignored.\n\nIn contemporary practice, RCT protocols move directly to bedside care through guideline committees. Trialists, statisticians, and consensus panels therefore occupy positions of substantial decision-making power. At that level of influence, semantics about “similar populations” and the difference between transport and generalization are no substitute for explicit causal modeling and transport analysis.",
  "title": "Generalizability vs. Transportability in Trials"
}