期刊論文
| 學年 | 101 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2012-10-10 |
| 作品名稱 | Assessing age-at-onset risk factors with incomplete covariate current status data under proportional odds models |
| 作品名稱（其他語言） | |
| 著者 | Wen, Chi-chung |
| 單位 | 淡江大學數學學系 |
| 出版者 | Chichester: John Wiley & Sons Ltd. |
| 著錄名稱、卷期、頁數 | Statistics in Medicine 32(12), p.2001-2012 |
| 摘要 | Motivated by an epidemiological survey of fracture in elderly women, we develop a semiparametric regression analysis of current status data with incompletely observed covariate under the proportional odds model. To accommodate both the interval-censored nature of current status failure time data and the incompletely observed covariate data, we propose an analysis based on the validation likelihood (VL), which is derived from likelihood pertaining to the validation sample, namely the subset of the sample where the data are completely observed. The missing data mechanism is assumed to be missing at random and is explicitly modeled and estimated in the VL approach. We propose implementing the VL method by integrating self-consistency and Newton-Raphson algorithms. Asymptotic normality and standard error estimation for the proposed estimator of the regression parameter are guaranteed. Simulation results reveal good performance of the VL estimator. The VL method has some gain in efficiency compared with the naive complete case method. But the VL method leads to unbiased estimators, whereas the complete case method does not when missing covariates are not missing completely at random. Application of the VL approach to the fracture data confirms that osteoporosis (low bone density) is a strong risk factor for the age at onset of fracture in elderly women. |
| 關鍵字 | fracture;interval censoring;missing at random;osteoporosis;semiparametric regression |
| 語言 | en |
| ISSN | 1097-0258 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | Chen, Yi-Hau |
| 審稿制度 | 是 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/97760 ) |