Missing data sensitivity analysis for recurrent event data using controlled imputation

Statistical analyses of recurrent event data have typically been based on the missing at random assumption. One implication of this is that, if data are collected only when patients are on their randomized treatment, the resulting de jure estimator of treatment effect corresponds to the situation in which the patients adhere to this regime throughout the study. For confirmatory analysis of clinical trials, sensitivity analyses are required to investigate alternative de facto estimands that depart from this assumption. Recent publications have described the use of multiple imputation methods based on pattern mixture models for continuous outcomes, where imputation for the missing data for one treatment arm (e.g. the active arm) is based on the statistical behaviour of outcomes in another arm (e.g. the placebo arm). This has been referred to as controlled imputation or reference‐based imputation. In this paper, we use the negative multinomial distribution to apply this approach to analyses of recurrent events and other similar outcomes. The methods are illustrated by a trial in severe asthma where the primary endpoint was rate of exacerbations and the primary analysis was based on the negative binomial model. Copyright © 2014 John Wiley & Sons, Ltd.     

产品侵权责任体系研究

产品侵权事件频发,严重损害了人们的人身、财产安全,亟需相关法律进行规制。《侵权责任法》作为侵权领域的基本法,其通过和实施使产品侵权责任体系初步建立起来,但由于相关法规的内容仍存在分歧,我国的产品侵权责任体系尚不够健全。从产品侵权责任的归责原则、构成要件、责任承担主体及惩罚性赔偿等方面对我国产品侵权责任体系的完善提出建议,以期对其发展有所裨益。     

从“宏大叙事”到“视域融合”:刑法因果关系问题的诠释学分析

传统因果关系理论着眼于为刑法因果关系问题提供稳定而统一的判断标准,然而因为其底层逻辑上的缺陷,最终所提供的实际上仅是对于刑法因果关系的描述。究其根源,是因为其自古典刑法以来所沿用的宏大叙事理解方式,这使得传统因果关系理论的根本问题不在于其具体理论是否能够细致地应对各种情况,而在于范式上需要完成彻底的转变。从诠释学的路径出发,能更有的放矢地揭示出对刑法因果关系问题的理解如何产生以及这种理解应当如何表述。视域融合的理解方式更贴合于刑法因果关系问题的本质,能够有效地沟通对因果关系问题的理解中的实然与应然。在具体理论层面,客观归责理论更适合视域融合理解方式的展开,能有效支撑刑法因果关系问题的范式转变。     

On the first order regression procedure of estimation for incomplete regression models

This article discusses some properties of the first order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesis testing. This work was carried out before Professor V.K. Srivastava passed away in 2001. The author is grateful to the referees for their illuminating comments on an earlier draft of this paper.     

Multiple Imputation for the Cox Proportional Hazards Model with Missing Covariates

We present three multiple imputation estimates for the Cox model with missing covariates. Two of the suggested estimates are asymptotically equivalent to estimates in the literature when the number of multiple imputations approaches infinity. The third estimate can be implemented using standard software that could handle time-varying covariates. This revised version was published online in July 2006 with corrections to the Cover Date.     

Generating missing values for simulation purposes: a multivariate amputation procedure

Missing data form a ubiquitous problem in scientific research, especially since most statistical analyses require complete data. To evaluate the performance of methods dealing with missing data, researchers perform simulation studies. An important aspect of these studies is the generation of missing values in a simulated, complete data set: the amputation procedure. We investigated the methodological validity and statistical nature of both the current amputation practice and a newly developed and implemented multivariate amputation procedure. We found that the current way of practice may not be appropriate for the generation of intuitive and reliable missing data problems. The multivariate amputation procedure, on the other hand, generates reliable amputations and allows for a proper regulation of missing data problems. The procedure has additional features to generate any missing data scenario precisely as intended. Hence, the multivariate amputation procedure is an efficient method to accurately evaluate missing data methodology.     

Matched case–control data analyses with missing covariates

We consider methods for analysing matched case–control data when some covariates ( W ) are completely observed but other covariates ( X ) are missing for some subjects. In matched case–control studies, the complete-record analysis discards completely observed subjects if none of their matching cases or controls are completely observed. We investigate an imputation estimate obtained by solving a joint estimating equation for log-odds ratios of disease and parameters in an imputation model. Imputation estimates for coefficients of W are shown to have smaller bias and mean-square error than do estimates from the complete-record analysis.     

Empirical Likelihood-based Inference in Linear Models with Missing Data

The missing response problem in linear regression is studied. An adjusted empirical likelihood approach to inference on the mean of the response variable is developed. A non-parametric version of Wilks's theorem for the adjusted empirical likelihood is proved, and the corresponding empirical likelihood confidence interval for the mean is constructed. With auxiliary information, an empirical likelihood-based estimator with asymptotic normality is defined and an adjusted empirical log-likelihood function with asymptotic χ² is derived. A simulation study is conducted to compare the adjusted empirical likelihood methods and the normal approximation methods in terms of coverage accuracies and average lengths of the confidence intervals. Based on biases and standard errors, a comparison is also made between the empirical likelihood-based estimator and related estimators by simulation. Our simulation indicates that the adjusted empirical likelihood methods perform competitively and the use of auxiliary information provides improved inferences.     

Multiple imputation methods for recurrent event data with missing event category

Frequently in clinical and epidemiologic studies, the event of interest is recurrent (i.e., can occur more than once per subject). When the events are not of the same type, an analysis which accounts for the fact that events fall into different categories will often be more informative. Often, however, although event times may always be known, information through which events are categorized may potentially be missing. Complete‐case methods (whose application may require, for example, that events be censored when their category cannot be determined) are valid only when event categories are missing completely at random. This assumption is rather restrictive. The authors propose two multiple imputation methods for analyzing multiple‐category recurrent event data under the proportional means/rates model. The use of a proper or improper imputation technique distinguishes the two approaches. Both methods lead to consistent estimation of regression parameters even when the missingness of event categories depends on covariates. The authors derive the asymptotic properties of the estimators and examine their behaviour in finite samples through simulation. They illustrate their approach using data from an international study on dialysis.