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The analysis of means (ANOM) is a method that can compare the mean of each treatment with the overall mean. As one-way ANOVA, the conventional ANOM is not robust under unequal variances. We applied two inference procedures, single-stage and modified single-stage sampling, to solve heteroscedastic analysis of means (HANOM) under unbalanced design. Using proposed weighted average across groups, the influence of the unknown mean and variance are both eliminated. To validate the stability of HANOM under several scenarios of unequal variances and sample sizes, simulation studies of empirical type I error rate are conducted to test the quality of procedures. A real application is provided for illustrating these two procedures clearly. In addition, we build an user-friendly interface to show the results of the HANOM by using Shiny package in R software. [ABSTRACT FROM AUTHOR]

Given k (≥2) independent and normally distributed populations with unknown means and unknown (and possibly unequal) variances, we used the single-stage sampling procedure for multiple comparison with a control to the problem of unequal sample sizes. A simulation of the family-wise error type I was conducted to validate the quality of the procedure and a numerical example was provided for illustrating this procedure. We provided a user-friendly interface by R shiny to obtain the critical values for the practitioners. [ABSTRACT FROM AUTHOR]

Recent studies have produced inconsistent findings regarding the association between community social vulnerability and COVID-19 incidence and death rates. This inconsistency may be due, in part, to the fact that these studies modeled cases and deaths separately, ignoring their inherent association and thus yielding imprecise estimates. To improve inferences, we develop a Bayesian multivariate negative binomial model for exploring joint spatial and temporal trends in COVID-19 infections and deaths. The model introduces smooth functions that capture long-term temporal trends, while maintaining enough flexibility to detect local outbreaks in areas with vulnerable populations. Using multivariate autoregressive priors, we jointly model COVID-19 cases and deaths over time, taking advantage of convenient conditional representations to improve posterior computation. As such, the proposed model provides a general framework for multivariate spatiotemporal modeling of counts and rates. We adopt a fully Bayesian approach and develop an efficient posterior Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs steps. We use the model to examine incidence and death rates among counties with high and low social vulnerability in the state of Georgia, USA, from 15 March to 15 December 2020. [ABSTRACT FROM AUTHOR]

Background: Retention in treatment for individuals with comorbid posttraumatic stress disorder (PTSD) and substance use disorders (SUD) is an area of concern in treatment outcome studies. The current study explores key variables related to retention in a group of women with comorbid PTSD and SUD enrolled in community SUD treatment randomized to eight weekly sessions of a trauma adapted mindfulness-based relapse prevention (TA-MBRP) or an integrated coping skills (ICS) group intervention. Methods: Two unadjusted and adjusted logistic discrete failure time (DFT) models were fit to examine associations between participants and the time (in weeks) to treatment completion status. Key covariates of interest, including time-varying PTSD Symptom Scale-Self Report (PSS) total score, time-varying Five Factors Mindfulness Questionnaire (FFMQ) total score, group assignment, baseline endorsements of substance use and demographics such as age, race and employment status were fit into the model.Results: In the adjusted PSS model, increased levels of PTSD symptom severity (PSS) scores at week 5 and 7 (PSS OR: 1:06: OR 1.13, respectively) were associated with higher odds of non-completion. In the FFMQ model, increased levels of FFMQ scores at week 6 (OR: 0:92) were associated with lower odds of non-completion. In both models, assignment to the ICS control group and unemployment were associated with lower odds of completion and baseline use of cocaine and sedatives were associated with higher odds of completion. Conclusion: Monitoring PTSD symptom severity and measures of mindfulness can inform providers on strategies to enhance retention early in treatment for individuals with comorbid PTSD/SUD. ClinicalTrials.gov # NCT02755103 [ABSTRACT FROM AUTHOR]