Coxmodeller

Coxmodeller is a hypothetical software package used in educational and illustrative contexts to demonstrate the application of Cox proportional hazards modeling to time-to-event data. It is designed to provide a clear workflow for fitting, diagnosing, and interpreting Cox models in survival analysis.

Key features typically associated with Coxmodeller include fitting through partial likelihood estimation, options for handling tied event times (such as Breslow and Efron methods), and support for both time-fixed and time-varying covariates. The framework commonly offers penalized likelihood approaches for high-dimensional data, including L1 and L2 regularization, to assist with variable selection. It also implements standard diagnostic and inference tools, such as AIC and BIC for model comparison, likelihood ratio tests, and tests of the proportional hazards assumption using Schoenfeld residuals. Model validation features like cross-validation and bootstrap resampling are often included, along with capabilities to compute risk scores and generate survival predictions for new observations.

Data input and interoperability typically emphasize compatibility with common formats and ecosystems. A Coxmodeller-style tool would usually accept CSV or TSV data, integrate with data-frame structures in major programming environments, and provide an API for fitting, prediction, and plotting. It aims to produce diagnostic plots (for example, Schoenfeld residuals or log-minus-log plots) to aid interpretation and assessment of model assumptions.

In practice, a Coxmodeller-like package serves researchers and students across epidemiology, clinical research, and social sciences for exploring time-to-event relationships, while highlighting the assumptions and limitations inherent in Cox modeling.