A Profile Conditional Likelihood Approach for the Semiparametric Transformation Regression Model with Missing Covariates

We propose a profile conditional likelihood approach to handle missing covariates in the general semiparametric transformation regression model. The method estimates the marginal survival function by the Kaplan-Meier estimator, and then estimates the parameters of the survival model and the covariate distribution from a conditional likelihood, substituting the Kaplan-Meier estimator for the marginal survival function in the conditional likelihood. This method is simpler than full maximum likelihood approaches, and yields consistent and asymptotically normally distributed estimator of the regression parameter when censoring is independent of the covariates. The estimator demonstrates very high relative efficiency in simulations. When compared with complete-case analysis, the proposed estimator can be more efficient when the missing data are missing completely at random and can correct bias when the missing data are missing at random. The potential application of the proposed method to the generalized probit model with missing continuous covariates is also outlined.     

An R package for model fitting,model selection and the simulation for longitudinal data with dropout missingness

Abstract

Missing data arise frequently in clinical and epidemiological fields, in particular in longitudinal studies. This paper describes the core features of an R package wgeesel, which implements marginal model fitting (i.e., weighted generalized estimating equations, WGEE; doubly robust GEE) for longitudinal data with dropouts under the assumption of missing at random. More importantly, this package comprehensively provide existing information criteria for WGEE model selection on marginal mean or correlation structures. Also, it can serve as a valuable tool for simulating longitudinal data with missing outcomes. Lastly, a real data example and simulations are presented to illustrate and validate our package.     

Goodness‐of‐fit tests for linear regression models with missing response data

The authors show how to test the goodness‐of‐fit of a linear regression model when there are missing data in the response variable. Their statistics are based on the L₂ distance between nonparametric estimators of the regression function and a ‐consistent estimator of the same function under the parametric model. They obtain the limit distribution of the statistics and check the validity of their bootstrap version. Finally, a simulation study allows them to examine the behaviour of their tests, whether the samples are complete or not.     

Strategies for Cohort Sampling Under the Cox Proportional Hazards Model,Application to an AIDS Clinical Trial

In some studies that relate covariates to times of failure it is not feasible to observe all covariates for all subjects. For example, some covariates may be too costly in terms of time, money, or effect on the subject to record for all subjects. This paper considers the relative efficiencies of several designs for sampling a portion of the cohort on which the costly covariates will be observed. Such designs typically measure all covariates for each failure and control for covariates of lesser interest. Control subjects are sampled either from risk sets at times of observed failures or from the entire cohort. A new design in which the sampling probability for each individual depends on the amount of information that the individual can contribute to estimated coefficients is shown to be superior to other sampling designs under certain conditions. Primary focus of our designs is on time-invariant covariates, but some methods easily generalize to the time-varying setting. Data from a study conducted by the AIDS Clinical Trials Group are used to illustrate the new sampling procedure and to explore the relative efficiency of several sampling schemes.     

《孔丛子》辑佚考实

《孔丛子》历来被视为伪书，但翻检唐宋类书，可裒寻到数条佚文。这些佚文屡见于先秦两汉其他书籍，有的是亡佚较早，注释者宋咸无暇罗致，有的则是宋咸故意删去的，还有的则属于版本流传过程中的自然亡佚。     

Dynamic Analysis of Recurrent Event Data with Missing Observations, with Application to Infant Diarrhoea in Brazil

Abstract.  This paper examines and applies methods for modelling longitudinal binary data subject to both intermittent missingness and dropout. The paper is based around the analysis of data from a study into the health impact of a sanitation programme carried out in Salvador, Brazil. Our objective was to investigate risk factors associated with incidence and prevalence of diarrhoea in children aged up to 3 years old. In total, 926 children were followed up at home twice a week from October 2000 to January 2002 and for each child daily occurrence of diarrhoea was recorded. A challenging factor in analysing these data is the presence of between-subject heterogeneity not explained by known risk factors, combined with significant loss of observed data through either intermittent missingness (average of 78 days per child) or dropout (21% of children). We discuss modelling strategies and show the advantages of taking an event history approach with an additive discrete time regression model.     

Incomplete covariates in the Cox model with applications to biological marker data

A common occurrence in clinical trials with a survival end point is missing covariate data. With ignorably missing covariate data, Lipsitz and Ibrahim proposed a set of estimating equations to estimate the parameters of Cox's proportional hazards model. They proposed to obtain parameter estimates via a Monte Carlo EM algorithm. We extend those results to non-ignorably missing covariate data. We present a clinical trials example with three partially observed laboratory markers which are used as covariates to predict survival.     

Analysis of longitudinal binary data from multiphase sampling

The efficient use of surrogate or auxiliary information has been investigated within both model-based and design-based approaches to data analysis, particularly in the context of missing data. Here we consider the use of such data in epidemiological studies of disease incidence in which surrogate measures of disease status are available for all subjects at two time points, but definitive diagnoses are available only in stratified subsamples. We briefly review methods for the analysis of two-phase studies of disease prevalence at a single time point, and we discuss the extension of four of these methods to the analysis of incidence studies. Their performance is compared with special reference to a study of the incidence of senile dementia.     

EMPIRICAL LIKELIHOOD‐BASED INFERENCES FOR PARTIALLY LINEAR MODELS WITH MISSING COVARIATES

This paper considers statistical inference for partially linear models Y = X ^? β +ν(Z) +? when the linear covariate X is missing with missing probability π depending upon (Y, Z). We propose empirical likelihood‐based statistics to construct confidence regions for β and ν(z). The resulting empirical likelihood ratio statistics are shown to be asymptotically chi‐squared‐distributed. The finite‐sample performance of the proposed statistics is assessed by simulation experiments. The proposed methods are applied to a dataset from an AIDS clinical trial.