69 research outputs found

    Sex-differential PTSD symptom trajectories across one year following suspected serious injury

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    Background Recent years have shown an increased application of prospective trajectory-oriented approaches to posttraumatic stress disorder (PTSD). Although women are generally considered at increased PTSD risk, sex and gender differences in PTSD symptom trajectories have not yet been extensively studied. Objective To perform an in-depth investigation of differences in PTSD symptom trajectories across one-year post-trauma between men and women, by interpreting the general trends of trajectories observed in sex-disaggregated samples, and comparing within-trajectory symptom course and prevalence rates. Method We included N = 554 participants (62.5% men, 37.5% women) from a multi-centre prospective cohort of emergency department patients with suspected severe injury. PTSD symptom severity was assessed at 1, 3, 6, and 12 months post-trauma, using the Clinician-Administered PTSD Scale for DSM-IV. Latent growth mixture modelling on longitudinal PTSD symptoms was performed within the sex-disaggregated and whole samples. Bayesian modelling with informative priors was applied for reliable model estimation, considering the imbalanced prevalence of the expected latent trajectories. Results In terms of general trends, the same trajectories were observed for men and women, i.e. resilient, recovery, chronic symptoms and delayed onset. Within-trajectory symptom courses were largely comparable, but resilient women had higher symptoms than resilient men. Sex differences in prevalence rates were observed for the recovery (higher in women) and delayed onset (higher in men) trajectories. Model fit for the sex-disaggregated samples was better than for the whole sample, indicating preferred application of sex-disaggregation. Analyses within the whole sample led to biased estimates of overall and sex-specific trajectory prevalence rates. Conclusions Sex-disaggregated trajectory analyses revealed limited sex differences in PTSD symptom trajectories within one-year post-trauma in terms of general trends, courses and prevalence rates. The observed biased trajectory prevalence rates in the whole sample emphasize the necessity to apply appropriate statistical techniques when conducting sex-sensitive research

    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

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    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

    Fieldwork Monitoring in Practice: Insights from 17 Large-scale Social Science Surveys in Germany

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    This study provides a synopsis of the current fieldwork monitoring practices of large-scale surveys in Germany. Based on the results of a standardized questionnaire, the study summarizes fieldwork monitoring indicators used and fieldwork measures carried out by 17 large-scale social sciences surveys in Germany. Our descriptive results reveal that a common set of fieldwork indicators and measures exist on which the studied surveys rely. However, it also uncovers the need for additional design-specific indicators. Finally, it underlines the importance of a close cooperation between survey representatives and fieldwork agencies to optimize processes in fieldwork monitoring in the German survey context. The article concludes with implications for fieldwork practice

    Predicting a Distal Outcome Variable From a Latent Growth Model: ML versus Bayesian Estimation

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    Latent growth models (LGMs) with a distal outcome allow researchers to assess longer-term patterns, and to detect the need to start a (preventive) treatment or intervention in an early stage. The aim of the current simulation study is to examine the performance of an LGM with a continuous distal outcome under maximum likelihood (ML) and Bayesian estimation with default and informative priors, under varying sample sizes, effect sizes and slope variance values. We conclude that caution is needed when predicting a distal outcome from an LGM when the: (1) sample size is small; and (2) amount of variation around the latent slope is small, even with a large sample size. We recommend against the use of ML and Bayesian estimation with Mplus default priors in these situations to avoid severely biased estimates. Recommendations for substantive researchers working with LGMs with distal outcomes are provided based on the simulation results

    Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners

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    Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics
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