Revisiting Multi-Subject Random Effects in fMRI: Advocating Prevalence Estimation

Random Effects analysis has been introduced into fMRI research in order to generalize findings from the study group to the whole population. Generalizing findings is obviously harder than detecting activation in the study group since in order to be significant, an activation has to be larger than the inter-subject variability. Indeed, detected regions are smaller when using random effect analysis versus fixed effects. The statistical assumptions behind the classic random effects model are that the effect in each location is normally distributed over subjects, and "activation" refers to a non-null mean effect. We argue this model is unrealistic compared to the true population variability, where, due to functional plasticity and registration anomalies, at each brain location some of the subjects are active and some are not. We propose a finite-Gaussian--mixture--random-effect. A model that amortizes between-subject spatial disagreement and quantifies it using the "prevalence" of activation at each location. This measure has several desirable properties: (a) It is more informative than the typical active/inactive paradigm. (b) In contrast to the hypothesis testing approach (thus t-maps) which are trivially rejected for large sample sizes, the larger the sample size, the more informative the prevalence statistic becomes. In this work we present a formal definition and an estimation procedure of this prevalence. The end result of the proposed analysis is a map of the prevalence at locations with significant activation, highlighting activations regions that are common over many brains

Modeling and Analysing Respondent Driven Sampling as a Counting Process

Respondent-driven sampling (RDS) is an approach to sampling design and analysis which utilizes the networks of social relationships that connect members of the target population, using chain-referral methods to facilitate sampling. RDS typically leads to biased sampling, favoring participants with many acquaintances. Naive estimates, such as the sample average, which are uncorrected for the sampling bias, will themselves be biased. To compensate for this bias, current methodology suggests inverse-degree weighting, where the "degree" is the number of acquaintances. This stems from the fundamental RDS assumption that the probability of sampling an individual is proportional to their degree. Since this assumption is tenuous at best, we propose to harness the additional information encapsulated in the time of recruitment, into a model-based inference framework for RDS. This information is typically collected by researchers, but ignored. We adapt methods developed for inference in epidemic processes to estimate the population size, degree counts and frequencies. While providing valuable information in themselves, these quantities ultimately serve to debias other estimators, such a disease's prevalence. A fundamental advantage of our approach is that, being model-based, it makes all assumptions of the data-generating process explicit. This enables verification of the assumptions, maximum likelihood estimation, extension with covariates, and model selection. We develop asymptotic theory, proving consistency and asymptotic normality properties. We further compare these estimators to the standard inverse-degree weighting through simulations, and using real-world data. In both cases we find our estimators to outperform current methods. The likelihood problem in the model we present is convex, and thus efficiently solvable. We implement these estimators in an R package, chords, available on CRAN.Comment: 16 page

Laparoscopic sacrohysteropexy and myomectomy for uterine prolapse: a case report and review of the literature

<p>Abstract</p> <p>Introduction</p> <p>A large number of hysterectomies are carried out for uterine prolapse, menorrhagia and other symptomatic but benign gynaecological conditions, which has increased interest in new approaches to treat these disorders. These new procedures are less invasive and offer reduced risk and faster recovery.</p> <p>Case presentation</p> <p>Sacrohysteropexy can be carried out instead of vaginal hysterectomy in the treatment of uterine prolapse. It involves using a synthetic mesh to suspend the uterus to the sacrum; this maintains durable anatomic restoration, normal vaginal axis and sexual function. A laparoscopic approach has major advantages over the abdominal route including shorter recovery time and less adhesion formation. We describe a laparoscopic sacrohysteropexy in a 55-year-old Caucasian British woman that was technically difficult. An intramural uterine fibroid was encroaching just above the uterosacral ligament making mesh positioning impossible. This was removed and the procedure completed successfully.</p> <p>Conclusion</p> <p>Posterior wall fibroid is not a contraindication for laparoscopic sacrohysteropexy. This procedure has increasingly become an effective treatment of uterine prolapse in women who have no indication for hysterectomy.</p

Determinants of emergency response willingness in the local public health workforce by jurisdictional and scenario patterns: a cross-sectional survey

<p>Abstract</p> <p>Background</p> <p>The all-hazards willingness to respond (WTR) of local public health personnel is critical to emergency preparedness. This study applied a threat-and efficacy-centered framework to characterize these workers' scenario and jurisdictional response willingness patterns toward a range of naturally-occurring and terrorism-related emergency scenarios.</p> <p>Methods</p> <p>Eight geographically diverse local health department (LHD) clusters (four urban and four rural) across the U.S. were recruited and administered an online survey about response willingness and related attitudes/beliefs toward four different public health emergency scenarios between April 2009 and June 2010 (66% response rate). Responses were dichotomized and analyzed using generalized linear multilevel mixed model analyses that also account for within-cluster and within-LHD correlations.</p> <p>Results</p> <p>Comparisons of rural to urban LHD workers showed statistically significant odds ratios (ORs) for WTR context across scenarios ranging from 1.5 to 2.4. When employees over 40 years old were compared to their younger counterparts, the ORs of WTR ranged from 1.27 to 1.58, and when females were compared to males, the ORs of WTR ranged from 0.57 to 0.61. Across the eight clusters, the percentage of workers indicating they would be unwilling to respond regardless of severity ranged from 14-28% for a weather event; 9-27% for pandemic influenza; 30-56% for a radiological 'dirty' bomb event; and 22-48% for an inhalational anthrax bioterrorism event. Efficacy was consistently identified as an important independent predictor of WTR.</p> <p>Conclusions</p> <p>Response willingness deficits in the local public health workforce pose a threat to all-hazards response capacity and health security. Local public health agencies and their stakeholders may incorporate key findings, including identified scenario-based willingness gaps and the importance of efficacy, as targets of preparedness curriculum development efforts and policies for enhancing response willingness. Reasons for an increased willingness in rural cohorts compared to urban cohorts should be further investigated in order to understand and develop methods for improving their overall response.</p

The ANTENATAL multicentre study to predict postnatal renal outcome in fetuses with posterior urethral valves: objectives and design

Abstract Background Posterior urethral valves (PUV) account for 17% of paediatric end-stage renal disease. A major issue in the management of PUV is prenatal prediction of postnatal renal function. Fetal ultrasound and fetal urine biochemistry are currently employed for this prediction, but clearly lack precision. We previously developed a fetal urine peptide signature that predicted in utero with high precision postnatal renal function in fetuses with PUV. We describe here the objectives and design of the prospective international multicentre ANTENATAL (multicentre validation of a fetal urine peptidome-based classifier to predict postnatal renal function in posterior urethral valves) study, set up to validate this fetal urine peptide signature. Methods Participants will be PUV pregnancies enrolled from 2017 to 2021 and followed up until 2023 in >30 European centres endorsed and supported by European reference networks for rare urological disorders (ERN eUROGEN) and rare kidney diseases (ERN ERKNet). The endpoint will be renal/patient survival at 2 years postnatally. Assuming α = 0.05, 1–β = 0.8 and a mean prevalence of severe renal outcome in PUV individuals of 0.35, 400 patients need to be enrolled to validate the previously reported sensitivity and specificity of the peptide signature. Results In this largest multicentre study of antenatally detected PUV, we anticipate bringing a novel tool to the clinic. Based on urinary peptides and potentially amended in the future with additional omics traits, this tool will be able to precisely quantify postnatal renal survival in PUV pregnancies. The main limitation of the employed approach is the need for specialized equipment. Conclusions Accurate risk assessment in the prenatal period should strongly improve the management of fetuses with PUV