When you need to understand how multiple factors—like age, dosage, or treatment type—independently affect the time until an event occurs, use this method. It is the standard approach for identifying significant contributors to survival outcomes while accounting for patients who were lost to follow-up or reached study end.
Understanding Multiple Risk Factors
Cox proportional hazards regression allows you to look beyond simple group comparisons. Instead of just asking if two groups differ, this tool identifies which specific factors are driving the difference in survival times. By including multiple variables at once, it isolates the independent effect of each factor, holding others constant.
The model produces a forest table of hazard ratios, which provides a clear, quantitative look at how each covariate influences the likelihood of an event occurring over time. This is essential for distinguishing between correlated variables and identifying true drivers of your data's outcome.
Evaluating Model Performance
The analysis returns a concordance index (C-index), which measures how well the model predicts the order of events. A C-index of 0.5 is no better than random guessing, while 0.7 is generally considered acceptable, 0.8 is good, and 0.9 or higher represents an excellent fit for the data.
Additionally, the model provides a likelihood ratio test to help you understand if the overall set of variables significantly explains the observed differences in survival time compared to a model with no variables at all.
Checking Model Assumptions
A central requirement for this method is that the hazard ratios must remain constant over the duration of the study. Lattice automatically performs Schoenfeld residual tests for each variable and provides a global p-value to check for violations.
If a violation is detected, the platform flags it clearly. Addressing these violations is important for academic integrity, as it ensures your findings are not skewed by effects that change significantly as time progresses.
Data Quality and Stability
To ensure reliable results, this method monitors the ratio of events to covariates. A common rule of thumb is that you should have at least 10 events for every variable in your model. Lattice issues warnings if your event count is low relative to the number of variables, helping you avoid overfitting or unstable estimates.
Because the tool handles data deterministically, it provides stable results that are ideal for inclusion in professional reports or research papers, ensuring that your conclusions about survival risks are supported by robust, verifiable calculations.
1 · Intent → method
An LLM picks survival_cox from a fixed catalog.
2 · Method → numbers
Deterministic Python engine runs the math. Same input → same output.
3 · Numbers → plain language
A second LLM translates the result into your domain’s vocabulary.
What does a 'hazard ratio' tell me in this analysis?
A hazard ratio represents the relative change in the risk of the event occurring for a one-unit increase in a specific variable. For example, a hazard ratio of 0.8 means a 20% reduction in the risk of the event per unit increase, while 1.2 would mean a 20% increase in risk.
What should I do if the proportional hazards assumption is violated?
If the model indicates a violation of the proportional hazards assumption, it means the effect of that specific variable changes over time. You should consider strategies such as stratifying by that variable or using more advanced time-varying models to ensure your results remain accurate.
Tool input schema
Schema for survival_cox not exported yet (run pnpm export:registry).