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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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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,...
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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.
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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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Predicting Individual Risk of Advanced Adenoma Based on the Interval-Censored Recurrent Adenoma Event and Informative

Yipeng Wei1, May AlHusseini1, Hormuzd A Katki2

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Summary

This study compares colorectal cancer screening prediction models. Frailty models offer superior accuracy with patient history, while marginal models perform better with limited data, enabling personalized screening intervals.

Keywords:
estimating equationsfrailty modelinterval‐censoredmarginal modelrecurrent eventsrisk prediction

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Area of Science:

  • Biostatistics
  • Cancer Epidemiology
  • Preventive Medicine

Background:

  • Panel count data is prevalent in cancer screening.
  • Colorectal cancer screening involves predicting advanced adenoma risk based on patient factors and history.

Purpose of the Study:

  • To compare prediction accuracy of joint frailty and marginal models for advanced adenoma risk.
  • To evaluate the impact of patient screening history on prediction performance.
  • To assess the potential of individualized screening intervals for earlier detection.

Main Methods:

  • Implementation of a joint frailty model with a non-stationary Poisson process and semi-parametric Cox models.
  • Estimation of coefficients and baseline intensity functions using estimating equations.
  • Comparison of predictions from frailty models (with and without subject-specific frailty) and marginal models.

Main Results:

  • Frailty models with subject-specific frailty outperform marginal models when patient screening history and adenoma events are available.
  • Marginal models show better performance in cases of early censoring and limited screening history.
  • Individualized screening intervals derived from dynamic predictions can lead to earlier adenoma detection.

Conclusions:

  • The choice of prediction model (frailty vs. marginal) depends on data availability and patient history.
  • Joint frailty models enhance prediction accuracy with sufficient patient-specific data.
  • Dynamic predictions support personalized screening strategies, improving early detection of colorectal adenomas.