加拿大问题赌博指数中文版的效度和信度

目的:评估加拿大问题赌博指数中文版(CPGI-C)的效度和信度。方法:选取43例匿名戒赌会成员和202例正常成人为研究对象,将其随机分为两半,分别进行探索性因子分析和验证性因子分析;以赌博相关认知量表(GRCS-C)、Barratt冲动性人格问卷(BIS-11)、抑郁自评量表(SDS)、网络成瘾量表修订版(CIAS-R)和DSM-IV多重反应问卷(DSM-IV-MR)作为效标工具;以受试者工作特征曲线(ROC)评估量表区分问题赌博者的特异度与灵敏度。2周后有5例匿名戒赌会成员和31例正常成人完成重测。结果:CPGI-C的问题赌博指数(PGSI)分量表共9个条目,探索性因子分析得出1个主成分因子,可解释总方差的74.3%;验证性因子分析显示,单因子结构模型的拟合指标良好(χ~2=2.087,CFI=0.926,TLI=0.963,GFI=0.926,IFI=0.978,NFI=0.958, RMSEA=0.094,SRMR=0.032);CPGI总分与各效标量表的总分均呈正相关(r=0.48～0.82,均P<0.001);ROC曲线下面积为0.962,划界分为9.5。CPGI-C的...     

Construct validity of the Eating Assessment Tool (EAT-10)

Abstract

Purpose: We aimed to evaluate the construct validity of the Eating Assessment Tool (EAT-10) by determining its dimensionality, rating scale integrity, item-person match, precision and relationship with the degree of airway invasion and functional oral intake.

Methods: We conducted a retrospective analysis of patients’ EAT-10 scores. We used the Rasch rating scale model. We investigated correlations between the EAT-10 and scores on the Penetration-Aspiration Scale (PAS) and Functional Oral Intake Scale (FOIS).

Results: The median score of the EAT-10 from 127 patients was 16 of 40 (range 0–40). Confirmatory factor analysis supported unidimensionality. The 5-point rating scale categories met published criteria. Two items misfit the Rasch model and two other items displayed differential item functioning. Rasch person reliability was 0.79. Our patient cohort was divided into three person-strata. Correlations between the EAT-10 and the PAS and FOIS were weak to moderate in strength (respectively: r?=?0.26, p?=?0.0036; r?=??0.27, p?=?0.0027).

Conclusions: Our analyses identified deficits in the construct validity of the EAT-10 suggestive of a need to improve the EAT-10 to support its frequent use in clinical practice and research.

• Implications for Rehabilitation

• Swallowing disorders are associated with severe complications, such as pneumonia and malnutrition, and impose both social and psychological burdens on patients.

• The Eating Assessment Tool is a self-report instrument developed to estimate initial dysphagia severity and monitor change in patient-reported dysphagia symptoms as a response to treatment.

• This study shows that the Eating Assessment Tool has deficits in its construct validity and a need to improve the instrument to support its frequent use in clinical practice and research.

     

Late-onset stress symptomatology (LOSS) scale – short form: development and validation

Objectives: Late-onset stress symptomatology (LOSS) is a phenomenon observed in older combat veterans who experience increased combat-related thoughts, feelings, and reminiscences corresponding with the changes and challenges of aging. Previously, we developed the LOSS Scale to assess LOSS. This paper describes the development and validation of a LOSS Scale short form (LOSS-SF) to screen veterans in various settings who may be actively re-examining their past wartime experiences.

Method: Three studies examined the reliability and validity of the LOSS-SF in separate samples of male combat veterans age 55 and older (total N = 346). Veterans were administered measures via telephone and mail survey. Correlation and regression analyses examined the reliability and validity of the LOSS-SF.

Results: The LOSS-SF exhibited strong internal consistency (alpha = .93), test-retest reliability (2 week interval on average; r = .88), and good concurrent validity with the LOSS Scale (r = .81). Convergent and divergent validity were supported by the pattern of correlations between the LOSS-SF and other construct measures.

Conclusion: The LOSS-SF is a reliable and valid measure to quickly assess thoughts, feelings, and reminiscences about past combat experiences in older veterans and identify those veterans in distress who may benefit from psychological interventions..     

Research methods in performance validity testing studies: Criterion grouping approach impacts study outcomes

Objective: Performance validity test (PVT) research studies commonly utilize a known-groups design, but the criterion grouping approaches within the design vary greatly from one study to another. At the present time, it is unclear as to what degree different criterion grouping approaches might impact PVT classification accuracy statistics. Method: To analyze this, the authors used three different criterion grouping approaches to examine how classification accuracy statistics of a PVT (Word Choice Test; WCT) would differ. The three criterion grouping approaches included: (1) failure of 2+ PVTs versus failure of 0 PVTs, (2) failure of 2+ PVTs versus failure of 0–1 PVT, and (3) failure of a stand-alone PVT versus passing of a stand-alone PVT (Test of Memory Malingering). Results: When setting specificity at ≥.90, WCT cutoff scores ranged from 41 to 44 and associated sensitivity values ranged from .64 to .88, depending on the criterion grouping approach that was utilized. Conclusions: When using a stand-alone PVT to define criterion group status, classification accuracy rates of the WCT were higher than expected, likely due to strong correlations between the reference PVT and the WCT. This held true even when considering evidence that this grouping approach results in higher rates of criterion group misclassification. Conversely, when using criterion grouping approaches that utilized failure of 2+ PVTs, accuracy rates were more consistent with expectations. These findings demonstrate that criterion grouping approaches can impact PVT classification accuracy rates and resultant cutoff scores. Strengths, weaknesses, and practical implications of each of the criterion grouping approaches are discussed.     

Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.