This paper presents a novel class of semiparametric estimating functions for the additive model with right-censored data that are obtained from general biased-sampling. The new estimator can be obtained using a weight...This paper presents a novel class of semiparametric estimating functions for the additive model with right-censored data that are obtained from general biased-sampling. The new estimator can be obtained using a weighted estimating equation for the covariate coefficients, by embedding the biased-sampling data into left-truncated and right-censored data. The asymptotic properties(consistency and asymptotic normality) of the proposed estimator are derived via the modern empirical processes theory. Based on the cumulative residual processes, we also propose graphical and numerical methods to assess the adequacy of the additive risk model.The good finite-sample performance of the proposed estimator is demonstrated by simulation studies and two applications of real datasets.展开更多
Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme...Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme is a cost-effective way to improve study efficiency. We develop a maximum semiparametric empirical likelihood estimation （MSELE） for data from a two-stage ODS scheme under the assumption that given covariate, the outcome follows a general linear model. The information of both validation samples and nonvalidation samples are used. What is more, we prove the asymptotic properties of the proposed MSELE.展开更多
A cost-effective sampling design is desirable in large cohort studies with a limited budget due to the high cost of measurements of primary exposure variables.The outcome-dependent sampling(ODS) designs enrich the obs...A cost-effective sampling design is desirable in large cohort studies with a limited budget due to the high cost of measurements of primary exposure variables.The outcome-dependent sampling(ODS) designs enrich the observed sample by oversampling the regions of the underlying population that convey the most information about the exposure-response relationship.The generalized linear models(GLMs) are widely used in many fields,however,much less developments have been done with the GLMs for data from the ODS designs.We study how to fit the GLMs to data obtained by the original ODS design and the two-phase ODS design,respectively.The asymptotic properties of the proposed estimators are derived.A series of simulations are conducted to assess the finite-sample performance of the proposed estimators.Applications to a Wilms tumor study and an air quality study demonstrate the practicability of the proposed methods.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.11771133 and 11401194)the Natural Science Foundation of Hunan Province of China(Grant No.2017JJ3021)+2 种基金Zhao’s work was supported by National Natural Science Foundation of China(Grant No.11771366)Zhou’s work was supported by the State Key Program of National Natural Science Foundation of China(Grant No.71331006)the State Key Program in the Major Research Plan of National Natural Science Foundation of China(Grant No.91546202)
文摘This paper presents a novel class of semiparametric estimating functions for the additive model with right-censored data that are obtained from general biased-sampling. The new estimator can be obtained using a weighted estimating equation for the covariate coefficients, by embedding the biased-sampling data into left-truncated and right-censored data. The asymptotic properties(consistency and asymptotic normality) of the proposed estimator are derived via the modern empirical processes theory. Based on the cumulative residual processes, we also propose graphical and numerical methods to assess the adequacy of the additive risk model.The good finite-sample performance of the proposed estimator is demonstrated by simulation studies and two applications of real datasets.
基金Jie-li DING is supported by the National Natural Science Foundation of China(No.11101314),Yan-yan LIU s supported by the National Natural Science Foundation of China(No.11171263,No.11371299)
文摘Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme is a cost-effective way to improve study efficiency. We develop a maximum semiparametric empirical likelihood estimation （MSELE） for data from a two-stage ODS scheme under the assumption that given covariate, the outcome follows a general linear model. The information of both validation samples and nonvalidation samples are used. What is more, we prove the asymptotic properties of the proposed MSELE.
基金National Natural Science Foundation of China(Grant Nos. 11571263,11371299 and 11101314)
文摘A cost-effective sampling design is desirable in large cohort studies with a limited budget due to the high cost of measurements of primary exposure variables.The outcome-dependent sampling(ODS) designs enrich the observed sample by oversampling the regions of the underlying population that convey the most information about the exposure-response relationship.The generalized linear models(GLMs) are widely used in many fields,however,much less developments have been done with the GLMs for data from the ODS designs.We study how to fit the GLMs to data obtained by the original ODS design and the two-phase ODS design,respectively.The asymptotic properties of the proposed estimators are derived.A series of simulations are conducted to assess the finite-sample performance of the proposed estimators.Applications to a Wilms tumor study and an air quality study demonstrate the practicability of the proposed methods.
