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<p>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.</p>

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Survival analysis for cardiothoracic surgeons: part 6-interpreting time-to-event data

  • 期刊论文

  • 发表日期：

    2025年3月8日

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  • H Shafeeq Ahmed

摘要

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.