Random Survival Forests › Disadvantages

One cost is interpretability. A fitted linear Cox model supplies coefficients that describe conditional hazard ratios under its assumptions. A survival forest supplies curves and, often, a single mortality score obtained by summing estimated cumulative hazard over a shared time grid. The score is not a hazard ratio or a probability, and its numerical scale depends on the grid. Permutation importance measures changes in a chosen predictive score; split-based importance measures a different quantity and can favor variables offering many candidate splits. Neither should be interpreted as a treatment effect. Partial dependence and predicted curves can help describe the fitted responses.

Another cost is the amount of information available within each leaf. Log-rank splitting uses event ordering and risk-set counts rather than the spacing between successive event times. Cox partial likelihood also relies on risk sets, so this fact alone does not establish that Cox uses more information or has a smaller mean squared error. A correctly specified proportional-hazards predictor can use its structure effectively, whereas a flexible forest must estimate many local curves. Small event counts and small late risk sets can make those estimates unstable. Event-count and leaf-size constraints help manage that trade-off, without guaranteeing precision.

Survival prediction also requires deliberate conventions. Our forest averages cumulative hazards and then exponentiates the negative average. Averaging the exponentiated tree hazards instead gives a different curve: Jensen's inequality places that average at or above the exponential of the average hazard. This comparison concerns the same tree hazard estimates, not a claim that averaged Kaplan–Meier curves equal either expression. The fitted staircases are flat after their last observed event, which supplies no evidence about later failures. The forest also does not solve informative censoring or automatically correct the consequences of pooling heterogeneous patients.