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The analysis of survival data can be challenging due to the presence of missing data. This paper proposes weighted composite quantile regression (CQR) for estimating a lot of quantile regression (QR) of survival data based on single-index coefficient model (SICM), which is a very general and flexible tool for exploring the relationship between response variable and a set of predictors. The statistical inference for SICM is considered when cause-of-failure information (censored or non-censored) is always observed. However, the cause-of-failure information may be missing at random (MAR) for various reasons. Regression calibration, imputation and inverse probability weighted approaches are applied to deal with the MAR assumption. The asymptotic normalities of the proposed estimators are established. Meanwhile, the oracle property of the variable selection based on adaptive LASSO penalty procedure is conducted. To assess the finite sample performance of the proposed estimators, simulation study with normal error and heavy-tail error are considered. As expected, the CQR estimators perform as good as the least-square estimators for normal error, and are more robust to heavy-tailed error. Finally, a breast cancer real data analysis is carried out to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

Missing data occur frequently in practice. Inverse probability weighting and imputation are regarded as two important approaches for handling missing data. However, the validity of these approaches depends on underlying model assumptions. A new general framework for multiply robust estimation procedures by combining multiple nonresponse and imputation models is proposed in the paper. The proposed method can be used to estimate both smooth and non-smooth parameters defined as the solution of some estimating equations. It includes population means, quantiles, and distribution functions as special cases. The asymptotic results of the proposed methods are established. The results of a simulation study and a real data application suggest that the proposed methods perform well in terms of bias and efficiency. [ABSTRACT FROM AUTHOR]

Generalized estimating equations (GEE) are useful tools for marginal regression analysis for longitudinal data. Having a high number of variables along with the presence of missing data presents complex issues when working in a longitudinal context. In variable selection for instance, penalized generalized estimating equations have not been systematically developed to integrate missing data. The MI-PGEE: multiple imputation-penalized generalized estimating equations, an extension of the multiple imputation-least absolute shrinkage and selection operator (MI-LASSO) is presented. MI-PGEE allows integration of missing data and within-subject correlation in variable selection procedures. Missing data are dealt with using multiple imputation, and variable selection is performed using a group LASSO penalty. Estimated coefficients for the same variable across multiply-imputed datasets are considered as a group while applying penalized generalized estimating equations, leading to a unique model across multiply-imputed datasets. In order to select the tuning parameter, a new BIC-like criterion is proposed. In a simulation study, the advantage of using MI-PGEE compared to simple imputation PGEE is shown. The usefulness of the new method is illustrated by an application to a subgroup of the placebo arm of the strontium ranelate efficacy in knee osteoarthritis trial study. [ABSTRACT FROM AUTHOR]

Clustered interval-censored failure time data can occur when the failure time of interest is collected from several clusters and known only within certain time intervals. Regression analysis of clustered interval-censored failure time data is discussed assuming that the data arise from the semiparametric additive hazards model. A multiple imputation approach is proposed for inference. A major advantage of the approach is its simplicity because it avoids estimating the correlation within clusters by implementing a resampling-based method. The presented approach can be easily implemented by using the existing software packages for right-censored failure time data. Extensive simulation studies are conducted, indicating that the proposed imputation approach performs well for practical situations. The proposed approach also performs well compared to the existing methods and can be more conveniently applied to various types of data representation. The proposed methodology is further demonstrated by applying it to a lymphatic filariasis study. [ABSTRACT FROM AUTHOR]

Abstract: Missing data often arise in practice. The commonly employed approach to handle the missing data is imputation, which is effective when the missing mechanism is known and each subject in the data set misses at random. However, the situation where the imputation is not appropriate often emerged. Because in that situation, some data are not missing at random, so a hybrid estimate, where the Bayesian and frequentist approaches are used for inferring the parameters with and without prior information respectively, is proposed. The asymptotic properties of the hybrid estimator are also provided. Numerical results including simulation studies and data analysis about grade point average (GPA) are conducted to show the performances of the proposed method. [Copyright &y& Elsevier]

Abstract: Multivariate extensions of well-known linear mixed-effects models have been increasingly utilized in inference by multiple imputation in the analysis of multilevel incomplete data. The normality assumption for the underlying error terms and random effects plays a crucial role in simulating the posterior predictive distribution from which the multiple imputations are drawn. The plausibility of this normality assumption on the subject-specific random effects is assessed. Specifically, the performance of multiple imputation created under a multivariate linear mixed-effects model is investigated on a diverse set of incomplete data sets simulated under varying distributional characteristics. Under moderate amounts of missing data, the simulation study confirms that the underlying model leads to a well-calibrated procedure with negligible biases and actual coverage rates close to nominal rates in estimates of the regression coefficients. Estimation quality of the random-effect variance and association measures, however, are negatively affected from both the misspecification of the random-effect distribution and number of incompletely-observed variables. Some of the adverse impacts include lower coverage rates and increased biases. [Copyright &y& Elsevier]