I developed a Cox proportional hazard regression model in R. Then I tried using
validate in Professor Harrell's
rms package to validate it:
> v <- validate(f5, B=200, dxy=TRUE)
dxy = -0.5510. Which got me confused. I was told that D can be computed from c index using the formula $2(c - 0.5)$, where $c$ estimates the probability of concordance between predicted and observed responses (p. 371 in Harrell, Lee and Mark 1996). A value of $0.5$ indicates no predictive discrimination and a value of $1.0$ indicates perfect separation of patients with different outcomes.
My question is: according to the formula, a negative $D$ would imply that $c$ is less than $0.5$, i.e. if A is predicted to live longer than B, then it is more likely than not that B would live longer than A(?!) Does it mean that my model is doing worse than random prediction? Or I am missing something here? Any help would be greatly appreciated.
- Harrell, Lee and Mark (1996) Multivariable Prognostic Models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors