Generalized Linear Mixed Models for Randomized Responses

Response bias (nonresponse and social desirability bias) is one of the main concerns when asking sensitive questions about behavior and attitudes. Self-reports on sensitive issues as in health research (e.g., drug and alcohol abuse), and social and behavioral sciences (e.g., attitudes against refugees, academic cheating) can be expected to be subject to considerable misreporting. To diminish misreporting on self-reports, indirect questioning techniques have been proposed such as the randomized response techniques. The randomized response techniques avoid a direct link between individual's response and the sensitive question, thereby protecting the individual's privacy. Next to the development of the innovative data collection methods, methodological advances have been made to enable a multivariate analysis to relate responses to sensitive questions to other variables. It is shown that the developments can be represented by a general response probability model (including all common designs) by extending it to a generalized linear model (GLM) or a generalized linear mixed model (GLMM). The general methodology is based on modifying common link functions to relate a linear predictor to the randomized response. This approach makes it possible to use existing software for GLMs and GLMMs to model randomized response data. The R-package GLMMRR makes the advanced methodology available to applied researchers. The extended models and software will seriously improve the application of the randomized response methodology. Three empirical examples are given to illustrate the methods

The Data Representativeness Criterion: Predicting the Performance of Supervised Classification Based on Data Set Similarity

In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when the training data used to build the algorithm is similar to new unseen data one wishes to apply it to. It is often unknown in advance how an algorithm will perform on new unseen data, being a crucial reason for not deploying an algorithm at all. Therefore, tools are needed to measure the similarity of data sets. In this paper, we propose the Data Representativeness Criterion (DRC) to determine how representative a training data set is of a new unseen data set. We present a proof of principle, to see whether the DRC can quantify the similarity of data sets and whether the DRC relates to the performance of a supervised classification algorithm. We compared a number of magnetic resonance imaging (MRI) data sets, ranging from subtle to severe difference is acquisition parameters. Results indicate that, based on the similarity of data sets, the DRC is able to give an indication as to when the performance of a supervised classifier decreases. The strictness of the DRC can be set by the user, depending on what one considers to be an acceptable underperformance.Comment: 12 pages, 6 figure

Systematically Defined Informative Priors in Bayesian Estimation:An Empirical Application on the Transmission of Internalizing Symptoms Through Mother-Adolescent Interaction Behavior

Background Bayesian estimation with informative priors permits updating previous findings with new data, thus generating cumulative knowledge. To reduce subjectivity in the process, the present study emphasizes how to systematically weigh and specify informative priors and highlights the use of different aggregation methods using an empirical example that examined whether observed mother-adolescent positive and negative interaction behavior mediate the associations between maternal and adolescent internalizing symptoms across early to mid-adolescence in a 3-year longitudinal multi-method design. Methods The sample consisted of 102 mother-adolescent dyads (39.2% girls, M-age T1 = 13.0). Mothers and adolescents reported on their internalizing symptoms and their interaction behaviors were observed during a conflict task. We systematically searched for previous studies and used an expert-informed weighting system to account for their relevance. Subsequently, we aggregated the (power) priors using three methods: linear pooling, logarithmic pooling, and fitting a normal distribution to the linear pool by means of maximum likelihood estimation. We compared the impact of the three differently specified informative priors and default priors on the prior predictive distribution, shrinkage, and the posterior estimates. Results The prior predictive distributions for the three informative priors were quite similar and centered around the observed data mean. The shrinkage results showed that the logarithmic pooled priors were least affected by the data. Most posterior estimates were similar across the different priors. Some previous studies contained extremely specific information, resulting in bimodal posterior distributions for the analyses with linear pooled prior distributions. The posteriors following the fitted normal priors and default priors were very similar. Overall, we found that maternal, but not adolescent, internalizing symptoms predicted subsequent mother-adolescent interaction behavior, whereas negative interaction behavior seemed to predict subsequent internalizing symptoms. Evidence regarding mediation effects remained limited. Conclusion A systematic search for previous information and an expert-built weighting system contribute to a clear specification of power priors. How information from multiple previous studies should be included in the prior depends on theoretical considerations (e.g., the prior is an updated Bayesian distribution), and may also be affected by pragmatic considerations regarding the impact of the previous results at hand (e.g., extremely specific previous results)

Bayesian statistics and modelling

Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade

Sodium Thiosulfate in Acute Myocardial Infarction:A Randomized Clinical Trial

In this proof-of-principle trial, the hypothesis was investigated that sodium thiosulfate (STS), a potent antioxidant and hydrogen sulfide donor, reduces reperfusion injury. A total of 373 patients presenting with a first ST-segment elevation myocardial infarction received either 12.5 g STS intravenously or matching placebo at arrival at the hospital and 6 hours later. The primary outcome, infarct size, measured by cardiac magnetic resonance at 4 months after randomization, did not differ between the treatment arms. Secondary outcomes were comparable as well, suggesting no clinical benefit of STS in this population at relatively low risk for large infarction.</p

Sodium Thiosulfate in Acute Myocardial Infarction:A Randomized Clinical Trial

In this proof-of-principle trial, the hypothesis was investigated that sodium thiosulfate (STS), a potent antioxidant and hydrogen sulfide donor, reduces reperfusion injury. A total of 373 patients presenting with a first ST-segment elevation myocardial infarction received either 12.5 g STS intravenously or matching placebo at arrival at the hospital and 6 hours later. The primary outcome, infarct size, measured by cardiac magnetic resonance at 4 months after randomization, did not differ between the treatment arms. Secondary outcomes were comparable as well, suggesting no clinical benefit of STS in this population at relatively low risk for large infarction.</p

Sodium Thiosulfate in Acute Myocardial Infarction:A Randomized Clinical Trial

In this proof-of-principle trial, the hypothesis was investigated that sodium thiosulfate (STS), a potent antioxidant and hydrogen sulfide donor, reduces reperfusion injury. A total of 373 patients presenting with a first ST-segment elevation myocardial infarction received either 12.5 g STS intravenously or matching placebo at arrival at the hospital and 6 hours later. The primary outcome, infarct size, measured by cardiac magnetic resonance at 4 months after randomization, did not differ between the treatment arms. Secondary outcomes were comparable as well, suggesting no clinical benefit of STS in this population at relatively low risk for large infarction.</p

Rationale and Design of the Groningen Intervention Study for the Preservation of Cardiac Function with Sodium Thiosulfate after ST-segment Elevation Myocardial Infarction (GIPS-IV) Trial

RATIONALE: Ischemia and subsequent reperfusion cause myocardial injury in patients presenting with ST-segment elevation myocardial infarction (STEMI). Hydrogen sulfide (H2S) reduces "ischemia-reperfusion injury" in various experimental animal models, but has not been evaluated in humans. This trial will examine the efficacy and safety of the H2S-donor sodium thiosulfate (STS) in patients presenting with a STEMI. STUDY DESIGN: The Groningen Intervention study for the Preservation of cardiac function with STS after STEMI (GIPS-IV) trial (NCT02899364) is a double-blind, randomized, placebo-controlled, multicenter trial, which will enroll 380 patients with a first STEMI. Patients receive STS 12.5 gram intravenously or matching placebo in addition to standard care immediately at arrival at the catheterization laboratory after providing consent. A second dose is administered 6 hours later at the coronary care unit. The primary endpoint is myocardial infarct size as quantified by cardiac magnetic resonance imaging 4 months after randomization. Secondary endpoints include the effect of STS on peak CK-MB during admission and left ventricular ejection fraction and NT-proBNP levels at 4 months follow-up. Patients will be followed-up for 2 years to assess clinical endpoints. CONCLUSIONS: The GIPS-IV trial is the first study to determine the effect of a H2S-donor on myocardial infarct size in patients presenting with STEMI