A Multiple Imputation Approach to Cox Regression with Interval-Censored Data
Version of Record online: 25 MAY 2004
Volume 56, Issue 1, pages 199–203, March 2000
How to Cite
Pan, W. (2000), A Multiple Imputation Approach to Cox Regression with Interval-Censored Data. Biometrics, 56: 199–203. doi: 10.1111/j.0006-341X.2000.00199.x
- Issue online: 25 MAY 2004
- Version of Record online: 25 MAY 2004
- Received March 1999. Revised May and July 1999. Accepted September 1999.
- Asymptotic normal data augmentation;
- Poor man's data augmentation;
- Proportional hazards model
Summary. We propose a general semiparametric method based on multiple imputation for Cox regression with interval-censored data. The method consists of iterating the following two steps. First, from finite-interval-censored (but not right-censored) data, exact failure times are imputed using Tanner and Wei's poor man's or asymptotic normal data augmentation scheme based on the current estimates of the regression coefficient and the baseline survival curve. Second, a standard statistical procedure for right-censored data, such as the Cox partial likelihood method, is applied to imputed data to update the estimates. Through simulation, we demonstrate that the resulting estimate of the regression coefficient and its associated standard error provide a promising alternative to the nonparametric maximum likelihood estimate. Our proposal is easily implemented by taking advantage of existing computer programs for right–censored data.