| 1. |
- Shang, Shulian, et al.
(author)
-
Partially linear single index Cox regression model in nested case-control studies
- 2013
-
In: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473 .- 1872-7352. ; 67, s. 199-212
-
Journal article (peer-reviewed)abstract
- The nested case-control (NCC) design is widely used in epidemiologic studies as a cost-effective subcohort sampling method to study the association between a disease and its potential risk factors. NCC data are commonly analyzed using Thomas' partial likelihood approach under the Cox proportional hazards model assumption. However, the linear modeling form in the Cox model may be insufficient for practical applications, especially when there are a large number of risk factors under investigation. In this paper, we consider a partially linear single index proportional hazards model, which includes a linear component for covariates of interest to yield easily interpretable results and a nonparametric single index component to adjust for multiple confounders effectively. We propose to approximate the nonparametric single index function by polynomial splines and estimate the parameters of interest using an iterative algorithm based on the partial likelihood. Asymptotic properties of the resulting estimators are established. The proposed methods are evaluated using simulations and applied to an NCC study of ovarian cancer.
|
|
| 2. |
- Liu, Mengling, et al.
(author)
-
Estimation and selection of complex covariate effects in pooled nested case-control studies with heterogeneity
- 2013
-
In: Biostatistics. - : Oxford University Press. - 1465-4644 .- 1468-4357. ; 14:4, s. 682-694
-
Journal article (peer-reviewed)abstract
- A major challenge in cancer epidemiologic studies, especially those of rare cancers, is observing enough cases. To address this, researchers often join forces by bringing multiple studies together to achieve large sample sizes, allowing for increased power in hypothesis testing, and improved efficiency in effect estimation. Combining studies, however, renders the analysis difficult owing to the presence of heterogeneity in the pooled data. In this article, motivated by a collaborative nested case-control (NCC) study of ovarian cancer in three cohorts from United States, Sweden, and Italy, we investigate the use of penalty regularized partial likelihood estimation in the context of pooled NCC studies to achieve two goals. First, we propose an adaptive group lasso (gLASSO) penalized approach to simultaneously identify important variables and estimate their effects. Second, we propose a composite agLASSO penalized approach to identify variables with heterogeneous effects. Both methods are readily implemented with the group coordinate gradient decent algorithm and shown to enjoy the oracle property. We conduct simulation studies to evaluate the performance of our proposed approaches in finite samples under various heterogeneity settings, and apply them to the pooled ovarian cancer study.
|
|
