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Survival Curves

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...

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Related Experiment Video

Updated: May 10, 2026

Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve

Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve

Published on: August 15, 2025

Measuring the surgical 'learning curve': methods, variables and competency.

Nuzhath Khan¹, Hamid Abboudi, Mohammed Shamim Khan

¹MRC Centre for Transplantation, King's College London, King's Health Partners, Department of Urology, Guy's Hospital, London, UK.

BJU International

|July 4, 2013

Summary

This summary is machine-generated.

Standardizing surgical learning curves (LCs) is crucial for patient safety and surgical education. Defining variables and controlling confounders like experience and case mix are essential for accurate competency assessment.

Keywords:

education learning curve urology

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Last Updated: May 10, 2026

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Published on: March 21, 2018

Area of Science:

Surgical Education
Medical Competency Assessment
Procedural Learning

Background:

Learning curves (LCs) are used to assess surgical skill acquisition.
Current methods for establishing LCs lack standardization.
The value of LCs as a measure of competency requires further evaluation.

Purpose of the Study:

To describe the measurement of surgical learning curves.
To identify procedural variables used in establishing learning curves.
To assess the utility of learning curves as a measure of surgical competency.

Main Methods:

A comprehensive review of surgical literature on learning curves was performed.
Databases searched include Medline and OVID.
Focus on identifying variables and methodologies for LC assessment.

Main Results:

Standardization of methods and variables is needed for measuring surgical LCs.
Key variables include trainee experience, supervision level, case mix, and procedure complexity.
Controlling for confounding variables and defining expert competency levels are critical.
Simulation and objective assessments can aid in LC studies.

Conclusions:

Surgical learning curves offer potential benefits for patient safety and education.
Standardization of LC measurement is essential for reliable competency assessment.
Clear definitions of competency and expert performance are required.