Defining survival epidemiology: postdiagnosis population science for people living with disease

OBJECTIVES: Epidemiology is largely organized to explain who becomes ill, yet many clinical and public health decisions occur after diagnosis. I introduce and formally define survival epidemiology as a new branch of science focused on assessing how people live longer and better with established disease, and I provide justification that prevention estimates should not be assumed to apply postdiagnosis. STUDY DESIGN AND SETTING: Conceptual and methodological commentary synthesizing evidence across cardiovascular, renal, oncologic, pulmonary, and hepatic conditions and integrating causal-inference and time-to-event principles for postdiagnosis questions. RESULTS: Across diseases, associations measured for incidence often fail to reproduce, and sometimes reverse, among patients with established disease. Diagnosis acts as a causal threshold that changes time scales and bias structures, including conditioning on disease (collider stratification), time-dependent confounding, immortal time bias, and reverse causation. Credible postdiagnosis inference requires designs that emulate randomized trials; explicit alignment of time zero with clinical decision points; strategies defined as used in practice; and handling of competing risks, multistate transitions, and longitudinal biomarkers (including joint models when appropriate). Essential postdiagnosis data include stage, molecular subtype, prior therapy lines, dose intensity and modifications, adverse events, performance status, and patient-reported outcomes. Recommended practice is parallel estimation of prevention and postdiagnosis survival effects for the same exposure-disease pairs and routine reporting of heterogeneity by stage, subtype, treatment pathway, and time since diagnosis. CONCLUSION: Prevention and postdiagnosis survival are distinct inferential targets. Journals should require clarity on whether claims pertain to prevention or survival and report target-trial elements; guideline bodies should distinguish prevention from survival recommendations when evidence allows; and funders, training programs, and public communication should support survival-focused methods, data standards, and context-specific messaging for people living with disease.