Bayesian Time-Series Models in Single Case Experimental Designs: A Tutorial for Trauma Researchers

Open Practices Statement: We analyzed archival data that are not under our direct control but that were already published and available to the public. We also simulated data for the illustration dataset in the Supplemental Materials. Our complete analysis data, scripts, and codes can be freely downloaded and modified for researchers’ personal use from Github (https://github.com/prathiba-stat/BITS-BUCP). Thus, both data and scripts are freely available for public use.Copyright © 2020 The Authors. Single case experimental designs (SCEDs) involve obtaining repeated measures from one or few participants before, during, and (sometimes) after treatment implementation. SCEDs are gaining popularity in trauma treatment research because they are cost-, time-, and resource-efficient and can provide robust causal evidence for more large-scale research. However, sophisticated techniques to analyse SCED data remain underutilized. The purpose of this tutorial paper is to discuss the utility of SCED data for trauma research, provide recommendations for addressing challenges specific to SCED approaches, and introduce a tutorial for two Bayesian models – the Bayesian interrupted time-series (BITS) model and Bayesian unknown change-point (BUCP) model – that can be used to analyse the typically small sample, autocorrelated, SCED data. Software codes are provided for the ease of guiding the readers on estimating these models. Analyses of a dataset from a published article as well as a trauma-specific simulated dataset are used to illustrate the models and demonstrate interpretation of results. We further discuss the implications of using such small sample data-analytic techniques for SCEDs specific to trauma research

COVID Stress Factors, Willingness to be Vaccinated, and Reasons for Vaccination Hesitancy Amongst Youth and Ethnic Minorities: A Structural Equation Modeling Analysis

Data availability statement: Due to the sensitive nature of the data, the actual data are not available. However, the correlation matrix is available upon request.COVID-19 has caused psychological trauma. However, it is unclear whether these stresses are comparable across ethnicities, across age groups, and vaccination hesitancy. Moreover, the relationship between the different types of stresses and their relationship to vaccination hesitancy has not been studied. Using rigorous statistical methodology (structural equation modeling), we examined the measurement invariance of five COVID stress factors: danger of contamination, socioeconomic consequences, xenophobia, traumatic stress, compulsive checking, by ethnicity and age, and investigated their relationship to vaccination hesitancy, ethnicity, age, and expectations of contracting COVID using structural equation models on a UK sample. The instrument showed measurement invariance both with respect to ethnicity and vaccination hesitancy. Subjects with more stress and worry about contracting COVID had no more enthusiasm for getting vaccinated than the less stressed. Ethnic minorities were less stressed despite suffering higher morbidity and mortality. As age increases, so does the hesitancy to be vaccinated despite younger subjects reporting more compulsive checking. Vaccination hesitancy was related to fear of side effects and safety concerns. Public health campaigns should target younger populations to address their fears about and stress due to compulsive checking for COVID-19. These campaigns should also be designed to reduce vaccination hesitancy to increase vaccination rates, decrease active and passive carriers of the virus, and ultimately attain herd immunity.The author(s) received no financial support for the research, authorship, and/or publication of this article

High-resolution DNA copy number and gene expression analyses distinguish chromophobe renal cell carcinomas and renal oncocytomas

Contains fulltext : 80487.pdf (publisher's version ) (Open Access)BACKGROUND: The diagnosis of benign renal oncocytomas (RO) and chromophobe renal cell carcinomas (RCC) based on their morphology remains uncertain in several cases. METHODS: We have applied Affymetrix GeneChip Mapping 250 K NspI high-density oligoarrays to identify small genomic alterations, which may occur beyond the specific losses of entire chromosomes, and also Affymetrix GeneChip HG-U133 Plus2.0 oligoarrays for gene expression profiling. RESULTS: By analysing of DNA extracted from 30 chRCCs and 42 ROs, we have confirmed the high specificity of monosomies of chromosomes 1, 2, 6, 10, 13, 17 and 21 in 70-93% of the chRCCs, while ROs displayed loss of chromosome 1 and 14 in 24% and 5% of the cases, respectively. We demonstrated that chromosomal gene expression biases might correlate with chromosomal abnormalities found in chromophobe RCCs and ROs. The vast majority genes downregulated in chromophobe RCC were mapped to chromosomes 2, 6, 10, 13 and 17. However, most of the genes overexpressed in chromophobe RCCs were located to chromosomes without any copy number changes indicating a transcriptional regulation as a main event. CONCLUSION: The SNP-array analysis failed to detect recurrent small deletions, which may mark loci of genes involved in the tumor development. However, we have identified loss of chromosome 2, 10, 13, 17 and 21 as discriminating alteration between chromophobe RCCs and ROs. Therefore, detection of these chromosomal changes can be used for the accurate diagnosis in routine histology

Clusters of trauma types as measured by the Life Events Checklist for DSM–5.

Experiences of potentially traumatic events (PTE), commonly assessed with the Life Events Checklist for DSM-5 (LEC-5), can be both varied in pattern and type. An understanding of LEC-assessed PTE type clusters and their relation to psychopathology can enhance research feasibility (e.g., address low base rates for certain PTE types), research communication/comparisons via the use of common terminology, and nuanced trauma assessments/treatments. To this point, the current study examined (1) clusters of PTE types assessed by the LEC-5; and (2) differential relations of these PTE type clusters to mental health correlates (posttraumatic stress disorder [PTSD] severity, depression severity, emotion dysregulation, reckless and self-destructive behaviors [RSDBs]). A trauma-exposed community sample of 408 participants was recruited via Amazon’s Mechanical Turk (Mage = 35.90 years; 56.50% female). Network analyses indicated three PTE type clusters: Accidental/Injury Traumas (LEC-5 items 1, 2, 3, 4 and 12), Victimization Traumas (LEC-5 items 6, 8, and 9), and Predominant Death Threat Traumas (LEC-5 items 5, 7, 10, 11, 13-16). Multiple regression analyses indicated that the Victimization Trauma Cluster significantly predicted PTSD severity (β = .23, p <.001), depression severity (β = .20, p =.001), and negative emotion dysregulation (β = .22, p <.001); and the Predominant Death Threat Trauma Cluster significantly predicted engagement in RSDBs (β = .31, p <.001) and positive emotion dysregulation (β = .26, p <.001), accounting for the influence of other PTE Clusters. Results support three PTE type classifications as assessed by the LEC-5, with important clinical and research implications

Therapeutic impacts of recalling and processing positive autobiographical memories on posttrauma health: An open-label study

The processing of positive memories technique (PPMT) entails detailed narration and processing of specific positive autobiographical memories (AM) and has shown promise in improving posttraumatic stress disorder (PTSD) symptoms. We examined whether participants receiving PPMT reported decreases in PTSD and depressive symptom severity, negative affect levels/reactivity, posttrauma cognitions, and positive emotion dysregulation, as well as increases in positive affect levels/reactivity and the number of retrieved positive AMs across four PPMT sessions. Individuals (N = 70) recruited from the community completed surveys at baseline (pre-PPMT), each PPMT session, and after completing all four PPMT sessions. Multilevel linear growth models indicated session-to-session decreases in PTSD severity, β = −.17, p <.001; depressive symptom severity, β = −.13, p <.001; negative affect levels, β = −.13, p <.001; positive affect reactivity, β = −.14, p =.014; and posttrauma cognitions, β = −.12, p <.001; and session-to-session increases in negative affect reactivity, β =.18, p =.001. Paired-samples t tests indicated decreases in retrieved positive AMs, d = 0.40, p =.001, including specific positive AMs, and negative AMs, d = 0.23, p =.022, and increases in retrieved overgeneral positive AMs, d = −0.38, p =.002, from baseline to postintervention. Thus, PPMT may help decrease PTSD and depression severity, negative affect, posttrauma cognitions, and negative AM recall tendencies. Clinicians may need to incorporate additional skills into the PPMT framework to improve positive affect processes that can be sustained over time