Robust confidence regions for the semi-parametric regression model with responses missing at random

In this paper, a regression semi-parametric model is considered where responses are assumed to be missing at random. From the empirical likelihood function defined based on the rank-based estimating equation, robust confidence intervals/regions of the true regression coefficient are derived. Monte Carlo simulation experiments show that the proposed approach provides more accurate confidence intervals/regions compared to its normal approximation counterpart under different model error structure. The approach is also compared with the least squares approach, and its superiority is shown whenever the error distribution in the simulation study is heavy tailed or contaminated. Finally, a real data example is given to illustrate our proposed method.     

COMBINING HOUSEHOLD SURVEYS USING MASS IMPUTATION TO ESTIMATE POPULATION TOTALS

Pressure is often placed on statistical analysts to improve the accuracy of their population estimates. In response to this pressure, analysts have long exploited the potential to combine surveys in various ways. This paper develops a framework for combining surveys when data items from one of the surveys is mass imputed. The estimates from the surveys are combined using a composite estimator (CE). The CE accounts for the variability due to the imputation model and the surveys’ sampling schemes. Diagnostics for the validity of the imputation model are also discussed. We describe an application of combining the Australian Labour Force Survey and the National Aboriginal and Torres Strait Islander Health Survey to estimate employment characteristics about the Indigenous population. The findings suggest that combining these surveys is beneficial.     

A structured approach to choosing estimands and estimators in longitudinal clinical trials

An important evolution in the missing data arena has been the recognition of need for clarity in objectives. The objectives of primary focus in clinical trials can often be categorized as assessing efficacy or effectiveness. The present investigation illustrated a structured framework for choosing estimands and estimators when testing investigational drugs to treat the symptoms of chronic illnesses. Key issues were discussed and illustrated using a reanalysis of the confirmatory trials from a new drug application in depression. The primary analysis used a likelihood‐based approach to assess efficacy: mean change to the planned endpoint of the trial assuming patients stayed on drug. Secondarily, effectiveness was assessed using a multiple imputation approach. The imputation model—derived solely from the placebo group—was used to impute missing values for both the drug and placebo groups. Therefore, this so‐called placebo multiple imputation (a.k.a. controlled imputation) approach assumed patients had reduced benefit from the drug after discontinuing it. Results from the example data provided clear evidence of efficacy for the experimental drug and characterized its effectiveness. Data after discontinuation of study medication were not required for these analyses. Given the idiosyncratic nature of drug development, no estimand or approach is universally appropriate. However, the general practice of pairing efficacy and effectiveness estimands may often be useful in understanding the overall risks and benefits of a drug. Controlled imputation approaches, such as placebo multiple imputation, can be a flexible and transparent framework for formulating primary analyses of effectiveness estimands and sensitivity analyses for efficacy estimands. Copyright © 2012 John Wiley & Sons, Ltd.     

Rounding non-binary categorical variables following multivariate normal imputation: evaluation of simple methods and implications for practice

We study bias arising from rounding categorical variables following multivariate normal (MVN) imputation. This task has been well studied for binary variables, but not for more general categorical variables. Three methods that assign imputed values to categories based on fixed reference points are compared using 25 specific scenarios covering variables with k=3, …, 7 categories, and five distributional shapes, and for each k=3, …, 7, we examine the distribution of bias arising over 100,000 distributions drawn from a symmetric Dirichlet distribution. We observed, on both empirical and theoretical grounds, that one method (projected-distance-based rounding) is superior to the other two methods, and that the risk of invalid inference with the best method may be too high at sample sizes n≥150 at 50% missingness, n≥250 at 30% missingness and n≥1500 at 10% missingness. Therefore, these methods are generally unsatisfactory for rounding categorical variables (with up to seven categories) following MVN imputation.     

Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data

Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms.     

刑事责任能力本质之新解

因适用语境的不同,我国目前对于刑事责任能力本质的认识与大陆法系的观点形似而实异。＂刑事责任能力＂这一称谓与大陆法系的刑法理论体系是协调一致的,而与我国的刑法理论并不协调。刑事责任能力的本质在我国具有二重性,即犯罪能力与刑事归责能力;相应地,其地位也具有两重性,即犯罪主体的要素与刑事责任的条件。     

GRAPHICAL SENSITIVITY ANALYSIS WITH DIFFERENT METHODS OF IMPUTATION FOR A TRIAL WITH PROBABLE NON‐IGNORABLE MISSING DATA

Graphical sensitivity analyses have recently been recommended for clinical trials with non‐ignorable missing outcome. We demonstrate an adaptation of this methodology for a continuous outcome of a trial of three cognitive‐behavioural therapies for mild depression in primary care, in which one arm had unexpectedly high levels of missing data. Fixed‐value and multiple imputations from a normal distribution (assuming either varying mean and fixed standard deviation, or fixed mean and varying standard deviation) were used to obtain contour plots of the contrast estimates with their P‐values superimposed, their confidence intervals, and the root mean square errors. Imputation was based either on the outcome value alone, or on change from baseline. The plots showed fixed‐value imputation to be more sensitive than imputing from a normal distribution, but the normally distributed imputations were subject to sampling noise. The contours of the sensitivity plots were close to linear in appearance, with the slope approximately equal to the ratio of the proportions of subjects with missing data in each trial arm.     

侵权责任法归责原则体系分析

归责原则是侵权责任法内容和体系建立的基础,全面理解归责原则,才能对侵权责任法有深入的了解。我国侵权责任法包括过错责任、过错推定和严格责任三项归责原则,三原则之间以过错责任为基础建立起内在联系,层次分明,衔接过渡,具有逻辑性。     

产品责任原则的经济分析

作者认为，产品责任立法的基本宗旨是促使生产者和销售者将其强加于用户和消费者的外部成本内在化。从经济学角度分析，我国产品质量责任法同时规定了对生产者的严格责任原则和对销售者的契约原则及过失原则，从而为用户和消费者的选择适用提供了交易便利和自由。     

产品责任归责原则的法经济学分析

“合理人标准”或“善良家父标准”是传统侵权归责原则判断行为人是否有过错的一般性标准。但是,在产品责任领域将此标准适用到判断产品的状况即产品设计或警示是否有缺陷的问题上时,则缺乏合理性和说服力。要想科学全面地解读产品责任归责问题,我们可以借鉴的一个重要方法就是法经济学分析。法经济学通过对产品责任中成本与收益、风险与效用的经济分析,如运用汉德公式对产品责任人的过错分析,可以找出能够实现最优社会效益的行为模式和责任分担模式。