This is a relatively simple situation. If each of the 2 continuous predictors was modeled linearly, each has 2 different slopes provided by the Cox model: one for the reference level of
sex (its individual coefficient in the model) and one for the other level (the sum of its individual coefficient with its
sex interaction coefficient). That gives you 2 "simple slopes," one for each
sex, for each of those 2 continuous predictors.
As Cox models represent relative hazards, the choice of values for the 11 predictors not involved in interactions is arbitrary. It might be simplest to work with whatever baseline values the software assumes. (Different survival software packages can make different assumptions.)
Work with one of the 2 continuous predictors interacting with
sex at a time. For a display most directly related to "simple slopes" in a linear model with interactions, use a
predict()-type function to get linear-predictor estimates (with standard errors) over a useful range of its values, for both values of
sex. Plot the linear predictor, the log-hazard relative to the baseline condition, as a function of the continuous-predictor value separately for each value of
sex on the same graph. This will work even if you modeled the continuous predictor non-linearly (e.g., with a spline); you will get 2 curves instead of 2 straight lines.
Repeat in a separate graph for the second predictor interacting with
sex. As those 2 continuous predictors aren't interacting with each other, those graphs provide all the "simple slopes" that you need.
Instead of working in the log-hazard scale with the linear-predictor estimates, you could get corresponding plots of hazard ratios with respect to the baseline condition, which might be more intuitive to your audience. Or you could choose to display separate survival probability estimates at a representative time point for each
sex as a function of the values of a continuous predictor.
This can be implemented by providing sets of predictor values to the standard R
survfit.coxph() functions. If you will be doing a lot of such modeling, the tools in the R
rms package can make these types of displays easier to produce once you get over the initial learning curve.