Time Dependent Cox Hazards Model
The time-dependent Cox model is an extension of Cox proportional hazards regression in which covariate values, or their effects, are allowed to change during follow-up.
시간의존 Cox 모형은 추적 기간 동안 공변량 값이나 그 효과가 변할 수 있도록 Cox 비례위험회귀를 확장한 모형이다.
How do I include predictors that change over time in a survival analysis?
시간에 따라 변하는 예측변수를 생존분석에 어떻게 넣나요?
A Cox model with time-dependent covariates splits each subject's follow-up into intervals, using the covariate value current at each event time, so exposures such as treatment start or changing biomarkers are handled correctly. It also fits time-varying coefficients, which are used when the proportional hazards assumption fails for a covariate.
When to use Time Dependent Cox Hazards Model
- Exposures that start or change after baseline, such as treatment initiation
- Repeatedly measured covariates such as lab values during follow-up
- Avoiding immortal time bias from classifying exposure at baseline
When to use something else
- All covariates are fixed at baseline and hazards are proportional: use the standard Cox proportional hazards model
- The time-varying covariate is itself affected by prior treatment and confounds later treatment: use marginal structural models
Assumptions and requirements
- Data in counting-process (start, stop, event) format
- Covariate values are known at each event time without using future information
- Proportional hazards for time-fixed effects or explicit time-varying coefficients
- Independent censoring
What the analysis reports
- Hazard ratios with confidence intervals for time-fixed and time-dependent covariates
- Coefficients for covariate-by-time interactions when effects vary over time
- Schoenfeld residual tests of proportional hazards
- Likelihood ratio, Wald, and score tests
How to report it
Modeled as a time-dependent covariate, [exposure] was associated with an increased hazard of [event], HR = [value], 95% CI [[value], [value]], p = [value].
Related methods
- Cox Proportional Hazards Model
- Landmark Analysis
- Joint Model for Longitudinal and Survival Data
Run Time Dependent Cox Hazards Model on Quorant
- Data you need
- a table (CSV or Excel) with a time variable, an event indicator and predictor (feature) columns
- Price
- 250 credits for one analysis up to 10,000 rows (complexity level 2); larger or wider data costs more. Optional methodologist review: 1200 credits.
- Checking
- Python results are cross-checked against R reference outputs in every deployment
- Before you pay
- A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.