Name
Title | ||
|---|---|---|
A Multiple Imputation Approach For Semiparametric Cure Model With Interval Censored Data |
Abstract | ||
|---|---|---|
The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval censored, the estimation of this model is challenging due to its complex data structure. A multiple imputation algorithm is proposed to obtain parameter and variance estimates for both the cure probability and the survival distribution of the uncured patients. The proposed approach can be easily implemented in commonly used statistical softwares, such as R and SAS, and its performance is comparable to fully parametric methods via comprehensive simulation studies. For illustration, the approach is applied to the 2000-2010 Greater Georgia breast cancer data set from the Surveillance, Epidemiology, and End Results Program. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.csda.2016.01.013 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
multiple imputation | Econometrics,Data mining,Parametric methods,Survival data,Complex data type,Semiparametric model,Imputation (statistics),Statistics,Censoring (statistics),Mathematics | Journal |
Volume | Issue | ISSN |
99 | C | 0167-9473 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jie zhou | 1 | 0 | 0.34 |
| Jiajia Zhang | 2 | 26 | 16.64 |
| Alexander C. McLain | 3 | 1 | 0.69 |
| Bo Cai | 4 | 29 | 7.09 |