Regression Analysis of Misclassified Current Status Data with Informative Observation Times

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation times.It is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading results.In this paper,the authors consider such data and propose a frailty-based inference procedure.In particular,an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established.The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.

A New Approach for Regression Analysis of Multivariate Current Status Data with Informative Censoring

Regression analysis of interval-censored failure time data has recently attracted a great deal of attention partly due to their increasing occurrences in many fields.In this paper,we discuss a type of such data,multivariate current status data,where in addition to the complex interval data structure,one also faces dependent or informative censoring.For inference,a sieve maximum likelihood estimation procedure is developed and the proposed estimators of regression parameters are shown to be asymptotically consistent and efficient.For the implementation of the method,an EM algorithm is provided,and the results from an extensive simulation study demonstrate the validity and good performance of the proposed inference procedure.For an illustration,the proposed approach is applied to a tumorigenicity experiment.

Efficient estimation for additive hazards regression with bivariate current status data

This paper discusses efficient estimation for the additive hazards regression model when only bivariate current status data are available. Current status data occur in many fields including demographical studies and tumorigenicity experiments （Keiding, 1991; Sun, 2006） and several approaches have been proposed for the additive hazards model with univariate current status data （Linet M., 1998; Martinussen and Scheike, 2002）. For bivariate data, in addition to facing the same problems as those with univariate data, one needs to deal with the association or correlation between two related failure time variables of interest. For this, we employ the copula model and an efficient estimation procedure is developed for inference. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. An illustrative example is provided.

Efficient Estimation for Semiparametric Varying-Coefficient Partially Linear Regression Models with Current Status Data

This article considers a semiparametric varying-coefficient partially linear regression model with current status data. The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are conducted to examine the small-sample properties of the proposed estimates and a real dataset is used to illustrate our approach.

SIEVE LEAST SQUARES ESTIMATOR FOR PARTIAL LINEAR MODELS WITH CURRENT STATUS DATA

Current status data often arise in survival analysis and reliability studies, when a continuous response is reduced to an indicator of whether the response is greater or less than an observed random threshold value. This article considers a partial linear model with current status data. A sieve least squares estimator is proposed to estimate both the regression parameters and the nonparametric function. This paper shows, under some mild condition, that the estimators are strong consistent. Moreover, the parameter estimators are normally distributed, while the nonparametric component achieves the optimal convergence rate. Simulation studies are carried out to investigate the performance of the proposed estimates. For illustration purposes, the method is applied to a real dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.

A TWO-STAGE ESTIMATION ALGORITHM FOR A TYPE OF CURRENT STATUS DATA

Success-failure life tests are widely used in reliability engineering research to evaluate the storage life of products, where the observed data are the current status data, usually summarized as the form of ＂binomial life data＂. For this type of data, this paper proposes a two-stage algorithm to estimate some commonly used lifetime distributions. This Mgorithm is automatic, intuitively appealing and simple to implement. Simulation studies show that compared with some existing methods, the proposed algorithm is more stable and efficient, especially in small sample situations, and it can also be extended to deM with some complicated lifetime distributions.

Predicting Nitrogen Status of Rice Using Multispectral Data at Canopy Scale

Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.