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By default survival curves can’t be drawn from Cox regression because the Cox Proportional Hazards model does not estimate the baseline hazard function (the baseline hazard term cancels out in the partial likelihood function and is ignored when estimating model coefficients). The baseline hazard function is required to plot survival curves. For many ...


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The Breslow estimator (of the cumulative hazard function) uses the parameter estimates from the Cox model, it does not estimate separate parameters. So it is not "over adjusted" (unless the Cox model is overadjusted). However, the estimate of the baseline survival function is a function of the covariates. Therefore, if you don't take account of the ...


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The assumption of proportional hazards is not about the time-dependency of the covariate but about the one of its effect on the hazard. Say you have a covariate $X$ which does not vary over time. The Cox model is a model based on the hazard and is defined as: $$ h(t \mid X(t) ) = h_0(t) \exp(\beta X)\ $$ The terming proportional hazards comes from the ...


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The Kaplan-Meier curve is the non-parametric maximum likelihood estimate of the survival function. The significance test for differences in survival is the log-rank test. The log-rank test is the score test for the Cox model with the variables adjusted as strata, so they should provide very similar answers. Cox models can fit way more complicated survival ...


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