Second-order or "corrected" Akaike information criterion, often
known as AICc (see, e.g. Burnham and Anderson 2002). This is
defined as -2 log-likelihood + `(2*p*n)/(n - p -1)`

, whereas
the standard AIC is defined as -2 log-likelihood + `2*p`

,
where `p`

is the number of parameters and `n`

is the
sample size. The correction is intended to adjust AIC for
small-sample bias, hence it only makes a difference to the result
for small `n`

.

## Arguments

- object
Fitted model returned by

`flexsurvreg`

(or`flexsurvspline`

).- cens
Include censored observations in the sample size term (

`n`

) used in this calculation. See`BIC.flexsurvreg`

for a discussion of the issues with defining the sample size.- ...
Other arguments (currently unused).

## References

Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.