The structure of oppositionality: Response disposition and situational aspects

Background: The Amsterdam Scale of Oppositionality (ASO) is a recently developed self-report instrument to measure the full range of oppositionality. It was used to test the assumption that oppositionality can best be conceptualized as a combination of emotions and behaviors varying across contexts, i.e., with parents, peers and authority figures. Method: The sample consisted of 560 boys and 598 girls, aged 8 to 12 years. The thirty items of the ASO, grouped in item parcels, were analyzed using confirmatory factor analyses. Results: Results confirmed the main hypothesis. The best fitting models contained strongly related emotional and behavioral factors and three mutually related situational factors. Oppositionality appeared to be to a large extent situation-specific. Girls are more affected by the situation than boys and show less oppositionality only outside the family context. Conclusions: Results are discussed with respect to the concept of oppositionality, varying expectations for interpersonal consequences, and implications for clinical assessment and studies of inter-informant reliability

Evaluation of atezolizumab immunogenicity: Efficacy and safety (Part 2).

Antibody therapeutics can be associated with unwanted immune responses resulting in the development of anti-drug antibodies (ADA). Optimal methods to evaluate the potential effects of ADA on clinical outcomes in oncology are not well established. In this study, we assessed efficacy and safety, based on ADA status, in patients from over 10 clinical trials that evaluated the immune checkpoint inhibitor atezolizumab as a single agent or as combination therapy for several types of advanced cancers. ADA can only be observed post randomization, and imbalances in baseline prognostic factors can confound the interpretation of ADA impact. We applied methodology to account for the confounding effects of baseline clinical characteristics and survivorship bias on efficacy. Adjusted meta-analyses revealed that despite numerical differences in overall survival and progression-free survival between ADA-positive and ADA-negative patients from some studies, ADA-positive patients from studies with an overall treatment effect derived benefit from atezolizumab, compared with their adjusted controls. Based on large, pooled populations from atezolizumab monotherapy or combination studies, unadjusted descriptive analyses did not identify a clear relationship between ADA status and frequency or severity of adverse events. Data also suggested that any ADA impact is not driven by neutralizing activity. Collectively, this exploratory analysis suggests that the potential for ADA development should not impact treatment decisions with atezolizumab

A Markov chain Monte Carlo algorithm for multiple imputation in large surveys

Important empirical information on household behavior and household finances, used heavily by researchers, central banks, and for policy consulting, is obtained from surveys. However, various interdependent factors that can only be controlled to a limited extent lead to unit and item nonresponse, and missing data on certain items is a frequent source of difficulties in statistical practice. All the more, it is important to explore techniques for the imputation of large survey data. This paper presents the theoretical underpinnings of a Markov Chain Monte Carlo multiple imputation procedure and outlines important technical aspects of the application of MCMC-type algorithms to large socio-economic datasets. In an exemplary application it is found that MCMC algorithms have good convergence properties even on large datasets with complex patterns of missingness, and that the use of a rich set of covariates in the imputation models has a substantial effect on the distributions of key financial variables

A New Concurrent Calibration Method for Nonequivalent Group Design under Nonrandom Assignment

common-item design, concurrent calibration, IRT linking, item response theory, Monte Carlo expectation maximization (MCEM) algorithm, multinomial logistic regression model, nonignorable missingness,