Related Experiment Video
Updated: Jan 7, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Beyond the blank page: Frequentist and Bayesian perspectives on risk prediction algorithms
Francisco Tustumi1,2, Felipe Antonio Boff Maegawa3, Pedro Luiz Serrano Uson Junior1
1Center for Personalized Medicine, Hospital Israelita Albert Einstein, São Paulo 05652900, Brazil.
Frequentist and Bayesian methods can enhance surgical risk prediction. Combining these approaches offers more robust and interpretable tools for personalized surgical care.
Area of Science:
- Surgical Oncology
- Artificial Intelligence
- Biostatistics
Background:
- Risk prediction is crucial in surgical oncology for anticipating complications and tailoring care.
- Machine learning (ML) models are increasingly used for outcome prediction in surgery.
- Current frequentist ML models rely solely on observed data, potentially ignoring prior clinical knowledge.
Related Concept Videos
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Probability in Statistics
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Hazard Rate

