I'm really struggling to understand how to interpret my R outputs in terms of hazards ratios. I have ran a Cox proportional hazard regression to compare survival between 2 treatment groups (neutron and photon therapy) and I have adjusted for the biological site of cancer:

2015年2月26日 · Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates. Furthermore, it suggests a variance inflation factor. If other covariates in the model are correlated with the covariate of interest (as they will often be in observational studies but not in randomized studies), you'll have to take that into ...

Fit a Cox model (fit.unstrat) with all of the covariates and no stratifying variables. Use cox.zph(fit.unstrat) to check for violations of the proportional hazards assumption. If you see e.g. rx has p<0.05, then rx violates the proportional hazards

2021年2月12日 · I am trying to determine the assumptions of a time varying cox proportional hazards model i.e., the covariates are allowed to vary with time. The functional form of the partial likelihood is: $$\\pr...

2021年1月26日 · I am trying to fit a cox proportional hazard model where all of my covariates are categorical except for one. I am planning to do a forward/backward model building but am wondering whether it is sound to include the covariates as a non-factor for the model building?

2018年8月21日 · I use Cox regression (proportional hazards) to model survival for a cohort of patients. Patients are censored (alive (0), dead (1)). I was wondering how Cox regression uses censored data intuitively. I thought when alive (0), Cox model will just ignore them, but apparently it is not so simple.

The baseline hazard is like a nuisance parameter that Cox so cleverly eliminated from the problem using the proportional hazards assumption. Whatever method you would like to use for estimating the hazard function and/or the baseline hazard in the context of the model would require using the Cox form of the model which forces proportionality ...

2015年12月8日 · I have two variables in my Cox regression/survival analysis. One is binary (v1, 0,1), the other is essentially discrete (v2, 1-200, with 1 being least severe and 200 being most severe). Interpreting their individual effects are simple, but their interaction makes no …

2022年8月23日 · Tied event times don't require special handling in a discrete-time binomial model, unlike in a Cox model. Note that Harrell's examples are essentially discrete-time survival models, just with a lot more time values than you might usually see. Logistic regression makes a proportional-odds instead of a proportional-hazards assumption.

2023年3月11日 · $\begingroup$ I would maybe add to your interpretation that the model makes the constraint of proportional hazards over time, so for example, if the estimate is for being male compared to a female baseline, then males have exp($\beta$) times the hazard of females in general, so at any point in time.