ArticleIndian journal of thoracic and cardiovascular surgery2025
Survival analysis for cardiothoracic surgeons: part 6-interpreting time-to-event data.
Article in Indian journal of thoracic and cardiovascular surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Survival analysis is critical in clinical research, especially in cardiothoracic surgery, to assess outcomes and compare interventions. Certain key survival analysis tools, including Kaplan-Meier (KM) curves, log-rank tests, and Cox proportional hazards models, are helpful in providing insights into survival probabilities and risk factors. KM curves help analyze time-to-event data, estimating survival probabilities, while log-rank tests compare survival distributions across groups. Cox proportional hazards models identify covariates influencing survival and calculate hazard ratios, which quantify the relative risk associated with specific variables. Together, these methods enable clinicians to make evidence-based decisions, optimize treatments, and improve patient outcomes.
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