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author:("Wu, yu'anshan")
1.  Statistical Inference for a Two-Stage Outcome-Dependent Sampling Design with a Continuous Outcome 
Biometrics  2011;67(1):194-202.
The two-stage case-control design has been widely used in epidemiology studies for its cost-effectiveness and improvement of the study efficiency (White, 1982; Breslow and Cain, 1988). The evolution of modern biomedical studies has called for cost-effective designs with a continuous outcome and exposure variables. In this paper, we propose a new two-stage outcome-dependent sampling scheme with a continuous outcome variable, where both the first-stage data and the second-stage data are from outcome-dependent sampling schemes. We develop a semiparametric empirical likelihood estimation for inference about the regression parameters in the proposed design. Simulation studies were conducted to investigate the small sample behavior of the proposed estimator. We demonstrate that, for a given statistical power, the proposed design will require a substantially smaller sample size than the alternative designs. The proposed method is illustrated with an environmental health study conducted at National Institute of Health.
PMCID: PMC4106685  PMID: 20560938
Biased sampling; Empirical likelihood; Outcome dependent; Sample size; Two-stage design
2.  Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome 
Biostatistics (Oxford, England)  2011;12(3):521-534.
Two-stage design has long been recognized to be a cost-effective way for conducting biomedical studies. In many trials, auxiliary covariate information may also be available, and it is of interest to exploit these auxiliary data to improve the efficiency of inferences. In this paper, we propose a 2-stage design with continuous outcome where the second-stage data is sampled with an “outcome-auxiliary-dependent sampling” (OADS) scheme. We propose an estimator which is the maximizer for an estimated likelihood function. We show that the proposed estimator is consistent and asymptotically normally distributed. The simulation study indicates that greater study efficiency gains can be achieved under the proposed 2-stage OADS design by utilizing the auxiliary covariate information when compared with other alternative sampling schemes. We illustrate the proposed method by analyzing a data set from an environmental epidemiologic study.
PMCID: PMC3114654  PMID: 21252082
Auxiliary covariate; Kernel smoothing; Outcome-auxiliary-dependent sampling; 2-stage sampling design
3.  Additive–multiplicative rates model for recurrent events 
Lifetime data analysis  2010;16(3):353-373.
Recurrent events are frequently encountered in biomedical studies. Evaluating the covariates effects on the marginal recurrent event rate is of practical interest. There are mainly two types of rate models for the recurrent event data: the multiplicative rates model and the additive rates model. We consider a more flexible additive–multiplicative rates model for analysis of recurrent event data, wherein some covariate effects are additive while others are multiplicative. We formulate estimating equations for estimating the regression parameters. The estimators for these regression parameters are shown to be consistent and asymptotically normally distributed under appropriate regularity conditions. Moreover, the estimator of the baseline mean function is proposed and its large sample properties are investigated. We also conduct simulation studies to evaluate the finite sample behavior of the proposed estimators. A medical study of patients with cystic fibrosis suffered from recurrent pulmonary exacerbations is provided for illustration of the proposed method.
PMCID: PMC3199147  PMID: 20229314
Recurrent events; Rate regression; Additive–multiplicative rates model; Counting process; Empirical process
4.  Multivariate Failure Times Regression with a Continuous Auxiliary Covariate 
Journal of multivariate analysis  2010;101(3):679-691.
How to take advantage of the available auxiliary covariate information when the primary covariate of interest is not measured is a frequently encountered question in biomedical study. In this paper, we consider the multivariate failure times regression analysis in which the primary covariate is assessed only in a validation set but a continuous auxiliary covariate for it is available for all subjects in the study cohort. Under the frame of marginal hazard model, we propose to estimate the induced relative risk function in the nonvalidation set through kernel smoothing method and then obtain an estimated pseudo-partial likelihood function. The proposed estimated pseudo-partial likelihood estimator is shown to be consistent and asymptotically normal. We also give an estimator of the marginal cumulative baseline hazard function. Simulations are conducted to evaluate the finite sample performance of our proposed estimator. The proposed method is illustrated by analyzing a heart disease data from Studies of Left Ventricular Dysfunction (SOLVD).
PMCID: PMC3182102  PMID: 21966052
Multivariate Failure Times; Auxiliary Covariate; Pseudo-Partial Likelihood; Kernel Smoothing; Validation Sample

Results 1-4 (4)