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Related Concept Videos

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Updated: Jul 16, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Competition and medical groups. A survivor analysis.

W D Marder, S Zuckerman

    Journal of Health Economics
    |May 10, 1985
    PubMed
    Summary

    Optimal medical practice sizes evolved, with large multi-specialty groups remaining ideal. Later data suggest solo practices may also be optimal in certain regions, shifting equilibrium distributions.

    Area of Science:

    • Health Economics
    • Medical Practice Management
    • Econometrics

    Background:

    • The optimal size distribution of medical practices is a key factor in healthcare delivery and efficiency.
    • Previous research (Frech and Ginsburg, 1974) established a framework for analyzing medical practice survivor data.

    Purpose of the Study:

    • To examine changes in the distribution of medical practice sizes over distinct time periods (1965-1969, 1969-1975, 1975-1980).
    • To re-evaluate the optimality of different medical group sizes based on updated data.

    Main Methods:

    • Survivor analysis of medical practice data.
    • Examination of changes in the distribution of practice sizes across specified time intervals.

    Main Results:

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    • Through 1975, most medical group sizes were found to be optimal, with large multi-specialty groups (≥100 physicians) being strongly confirmed as optimal.
    • Data from 1975-1980 indicate a shift, with only large multi-specialty groups remaining in the optimal size range.
    • Evidence suggests solo practices may be optimal in specific geographic areas.

    Conclusions:

    • The equilibrium size distribution of medical practices has likely shifted over time.
    • Large multi-specialty groups remain a consistently optimal practice model.
    • Geographic factors may influence the optimality of solo medical practices.