Neural Representations of Observed Interpersonal Synchrony/Asynchrony in the Social Perception Network.

The visual perception of individuals is thought to be mediated by a network of regions in the occipitotemporal cortex that supports specialized processing of faces, bodies, and actions. In comparison, we know relatively little about the neural mechanisms that support the perception of multiple individuals and the interactions between them. The present study sought to elucidate the visual processing of social interactions by identifying which regions of the social perception network represent interpersonal synchrony. In an fMRI study with 32 human participants (26 female, 6 male), we used multivoxel pattern analysis to investigate whether activity in face-selective, body-selective, and interaction-sensitive regions across the social perception network supports the decoding of synchronous versus asynchronous head-nodding and head-shaking. Several regions were found to support significant decoding of synchrony/asynchrony, including extrastriate body area (EBA), face-selective and interaction-sensitive mid/posterior right superior temporal sulcus, and occipital face area. We also saw robust cross-classification across actions in the EBA, suggestive of movement-invariant representations of synchrony/asynchrony. Exploratory whole-brain analyses also identified a region of the right fusiform cortex that responded more strongly to synchronous than to asynchronous motion. Critically, perceiving interpersonal synchrony/asynchrony requires the simultaneous extraction and integration of dynamic information from more than one person. Hence, the representation of synchrony/asynchrony cannot be attributed to augmented or additive processing of individual actors. Our findings therefore provide important new evidence that social interactions recruit dedicated visual processing within the social perception network that extends beyond that engaged by the faces and bodies of the constituent individuals.

Statistical and Scientific Considerations Concerning the Interpretation, Replicability, and Transportability of Research Findings.

To advance scientific understanding of disease processes and related intervention effects, study results should be free from bias and replicable. More broadly, investigators seek results that are transportable, that is, applicable to a perceived study population as well as in other environments and populations. We review fundamental statistical issues that arise in the analysis of observational data from disease cohorts and other sources and discuss how these issues affect the transportability and replicability of research results. Much of the literature focuses on estimating average exposure or intervention effects at the population level, but we argue for more nuanced analyses of conditional effects that reflect the complexity of disease processes.

Results of clinical effectiveness of conventional versus Mirasol-treated Apheresis Platelets in Patients with Hypoproliferative Thrombocytopenia (MiPLATE) trial.

BACKGROUND: The Mirasol® Pathogen Reduction Technology System was developed to reduce transfusion-transmitted diseases in platelet (PLT) products. STUDY DESIGN AND METHODS: MiPLATE trial was a prospective, multicenter, controlled, randomized, non-inferiority (NI) study of the clinical effectiveness of conventional versus Mirasol-treated Apheresis PLTs in participants with hypoproliferative thrombocytopenia. The novel primary endpoint was days of ≥Grade 2 bleeding with an NI margin of 1.6. RESULTS: After 330 participants were randomized, a planned interim analysis of 297 participants (145 MIRASOL, 152 CONTROL) receiving ≥1 study transfusion found a 2.79-relative rate (RR) in the MIRASOL compared to the CONTROL in number of days with ≥Grade 2 bleeding (95% confidence interval [CI] 1.67-4.67). The proportion of subjects with ≥Grade 2 bleeding was 40.0% (n = 58) in MIRASOL and 30.3% (n = 46) in CONTROL (RR = 1.32, 95% CI 0.97-1.81, p = .08). Corrected count increments were lower (p < .01) and the number of PLT transfusion episodes per participant was higher (RR = 1.22, 95% CI 1.05-1.41) in MIRASOL. There was no difference in the days of PLT support (hazard ratio = 0.86, 95% CI 0.68-1.08) or total number of red blood cell transfusions (RR = 1.12, 95% CI 0.91-1.37) between MIRASOL versus CONTROL. Transfusion emergent adverse events were reported in 119 MIRASOL participants (84.4%) compared to 133 (82.6%) participants in CONTROL (p = NS). DISCUSSION: This study did not support that MIRASOL was non-inferior compared to conventional platelets using the novel endpoint number of days with ≥Grade 2 bleeding in MIRASOL when compared to CONTROL.

Quality of life and cost-effectiveness of convalescent plasma compared to standard care for hospitalized COVID-19 patients in the CONCOR-1 trial.

BACKGROUND: The CONvalescent Plasma for Hospitalized Adults With COVID-19 Respiratory Illness (CONCOR-1) trial was a multicenter randomized controlled trial assessing convalescent plasma in hospitalized COVID-19 patients. This study evaluates the cost-effectiveness of convalescent plasma and its impact on quality-of-life to provide insight into its potential as an alternative treatment in resource-constrained settings. METHODS: Individual patient data on health outcomes and resource utilization from the CONCOR-1 trial were used to conduct the analysis from the Canadian public payer's perspective with a time horizon of 30 days post-randomization. Baseline and 30-day EQ-5D-5L were measured to calculate quality-adjusted survival. All costs are presented in 2021 Canadian dollars. The base case assessed the EQ-5D-5L scores of hospitalized inpatients reporting at both timepoints, and a utility score of 0 was assigned for patients who died within 30 days. Costs for all patients enrolled were used. The sensitivity analysis utilizes EQ-5D-5L scores from the same population but only uses costs from this population. RESULTS: 940 patients were randomized: 627 received CCP and 313 received standard care. The total costs were $28,716 (standard deviation, $25,380) and $24,258 ($22,939) for the convalescent plasma and standard care arms respectively. EQ-5D-5L scores were 0.61 in both arms (p = .85) at baseline. At 30 days, EQ-5D-5L scores were 0.63 and 0.64 for patients in the convalescent plasma and standard care arms, respectively (p = .46). The incremental cost was $4458 and the incremental quality-adjusted life day was -0.078. DISCUSSION: Convalescent plasma was less effective and more costly than standard care in treating hospitalized COVID-19.

Two-phase designs with failure time processes subject to nonsusceptibility.

Epidemiological studies based on 2-phase designs help ensure efficient use of limited resources in situations where certain covariates are prohibitively expensive to measure for a full cohort. Typically, these designs involve 2 steps: In phase I, data on an outcome and inexpensive covariates are acquired, and in phase II, a subsample is chosen in which the costly variable of interest is measured. For right-censored data, 2-phase designs have been primarily based on the Cox model. We develop efficient 2-phase design strategies for settings involving a fraction of long-term survivors due to nonsusceptibility. Using mixture models accommodating a nonsusceptible fraction, we consider 3 regression frameworks, including (a) a logistic "cure" model, (b) a proportional hazards model for those who are susceptible, and (c) regression models for susceptibility and failure time in those susceptible. Importantly, we introduce a novel class of bivariate residual-dependent designs to address the unique challenges presented in scenario (c), which involves 2 parameters of interest. Extensive simulation studies demonstrate the superiority of our approach over various phase II subsampling schemes. We illustrate the method through applications to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Low-velocity penetrating brain injury: a review of the literature and illustrative case.

INTRODUCTION: Low-velocity penetrating brain injury (LVPBI) is a class of brain injury where a foreign object violates the skull and damages the brain. Such injuries are rare and consequently understudied. CASE: As such, we report an illustrative case of a 29-year-old female with a dense, plastic spike penetrating her right orbit and into her midbrain. After assessment with a CT scan and angiography, the object was removed with careful attention to possible vascular injury. The patient had an uncomplicated post-operative course and received antibiotic and antiepileptic prophylaxis. She was discharged on post-operative day 5, experiencing only mild left-sided weakness. DISCUSSION: Common concerns regarding LVPBI include infection, post-traumatic epilepsy, and vascular injury. A review of published LVPBI cases over the past 20 years demonstrated that most cases (55.2%) are due to accidents. Of patients undergoing surgery, 43.4% underwent a craniotomy, and 22.8% underwent a craniectomy. Despite the grave nature of LVPBI, only 13.5% of the patients died. Additionally, 6.5% of patients developed an infection over their clinical course. CONCLUSION: In all, more reported cases further paint a picture of the current state of management and outcomes regarding LVPBI, paving the way for more cohesive guidelines to ensure the best possible patient outcomes.

Benchmark dose profiles for bivariate exposures.

While benchmark dose (BMD) methodology is well-established for settings with a single exposure, these methods cannot easily handle multidimensional exposures with nonlinear effects. We propose a framework for BMD analysis to characterize the joint effect of a two-dimensional exposure on a continuous outcome using a generalized additive model while adjusting for potential confounders via propensity scores. This leads to a dose-response surface which can be summarized in two dimensions by a contour plot in which combinations of exposures leading to the same expected effect are identified. In our motivating study of prenatal alcohol exposure, cognitive deficits in children are found to be associated with both the frequency of drinking as well as the amount of alcohol consumed on each drinking day during pregnancy. The general methodological framework is useful for a broad range of settings, including combinations of environmental stressors, such as chemical mixtures, and in explorations of the impact of dose rate rather than simply cumulative exposure on adverse outcomes.

Assessing the accuracy of predictive models with interval-censored data.

We develop methods for assessing the predictive accuracy of a given event time model when the validation sample is comprised of case $K$ interval-censored data. An imputation-based, an inverse probability weighted (IPW), and an augmented inverse probability weighted (AIPW) estimator are developed and evaluated for the mean prediction error and the area under the receiver operating characteristic curve when the goal is to predict event status at a landmark time. The weights used for the IPW and AIPW estimators are obtained by fitting a multistate model which jointly considers the event process, the recurrent assessment process, and loss to follow-up. We empirically investigate the performance of the proposed methods and illustrate their application in the context of a motivating rheumatology study in which human leukocyte antigen markers are used to predict disease progression status in patients with psoriatic arthritis.

Spatial dependence modeling of latent susceptibility and time to joint damage in psoriatic arthritis.

Important scientific insights into chronic diseases affecting several organ systems can be gained from modeling spatial dependence of sites experiencing damage progression. We describe models and methods for studying spatial dependence of joint damage in psoriatic arthritis (PsA). Since a large number of joints may remain unaffected even among individuals with a long disease history, spatial dependence is first modeled in latent joint-specific indicators of susceptibility. Among susceptible joints, a Gaussian copula is adopted for dependence modeling of times to damage. Likelihood and composite likelihoods are developed for settings, where individuals are under intermittent observation and progression times are subject to type K interval censoring. Two-stage estimation procedures help mitigate the computational burden arising when a large number of processes (i.e., joints) are under consideration. Simulation studies confirm that the proposed methods provide valid inference, and an application to the motivating data from the University of Toronto Psoriatic Arthritis Clinic yields important insights which can help physicians distinguish PsA from arthritic conditions with different dependence patterns.

Two-phase designs with current status data.

We consider the design and analysis of two-phase studies aiming to assess the relation between a fixed (eg, genetic) marker and an event time under current status observation. We consider a common setting in which a phase I sample is comprised of a large cohort of individuals with outcome (ie, current status) data and a vector of inexpensive covariates. Stored biospecimens for individuals in the phase I sample can be assayed to record the marker of interest for individuals selected in a phase II sub-sample. The design challenge is then to select the phase II sub-sample in order to maximize the precision of the marker effect on the time of interest under a proportional hazards model. This problem has not been examined before for current status data and the role of the assessment time is highlighted. Inference based on likelihood and inverse probability weighted estimating functions are considered, with designs centered on score-based residuals, extreme current status observations, or stratified sampling schemes. Data from a registry of patients with psoriatic arthritis is used in an illustration where we study the risk of diabetes as a comorbidity.

Analysis of secondary failure time responses in studies with response-dependent sampling schemes.

Response-dependent sampling is routinely used as an enrichment strategy in the design of family studies investigating the heritable nature of disease. In addition to the response of primary interest, investigators often wish to investigate the association between biomarkers and secondary responses related to possible comorbidities. Statistical analysis regarding genetic biomarkers and their association with the secondary outcome must address the biased sampling scheme involving the primary response. In this article, we develop composite likelihoods and two-stage estimation procedures for such secondary analyses in which the within-family dependence structure for the primary and secondary outcomes is modeled via a Gaussian copula. The dependence among responses within family members is modeled based on kinship coefficients. Auxiliary data from independent individuals are exploited by augmenting the composite likelihoods to increase precision of marginal parameter estimates and enhance the efficiency of estimators of the dependence parameters. Simulation studies are carried out to evaluate the finite sample performance of the proposed method, and an application to a motivating family study in psoriatic arthritis is given for illustration.

Multistate models as a framework for estimand specification in clinical trials of complex processes.

Intensity-based multistate models provide a useful framework for characterizing disease processes, the introduction of interventions, loss to followup, and other complications arising in the conduct of randomized trials studying complex life history processes. Within this framework we discuss the issues involved in the specification of estimands and show the limiting values of common estimators of marginal process features based on cumulative incidence function regression models. When intercurrent events arise we stress the need to carefully define the target estimand and the importance of avoiding targets of inference that are not interpretable in the real world. This has implications for analyses, but also the design of clinical trials where protocols may help in the interpretation of estimands based on marginal features.

New late-emphasis and combination tests based on infimum and supremum logrank statistics with application in oncology trials.

Immunotherapy cancer clinical trials routinely feature an initial period during which the treatment is given without evident therapeutic benefit, which may be followed by a period during which an effective therapy reduces the hazard for event occurrence. The nature of this treatment effect is incompatible with the proportional hazards assumption, which has prompted much work on the development of alternative effect measures of frameworks for testing. We consider tests based on individual and combination of early- and late-emphasis infimum and supremum logrank statistics, describe how they can be implemented, and evaluate their performance in simulation studies. Through this work and illustrative applications we conclude that this class of test statistics offers a new and powerful framework for assessing treatment effects in cancer clinical trials involving immunotherapies.

Cathepsin L proteolytically processes histone H3 during mouse embryonic stem cell differentiation.

Chromatin undergoes developmentally-regulated structural and chemical changes as cells differentiate, which subsequently lead to differences in cellular function by altering patterns of gene expression. To gain insight into chromatin alterations that occur during mammalian differentiation, we turned to a mouse embryonic stem cell (ESC) model. Here we show that histone H3 is proteolytically cleaved at its N-terminus during ESC differentiation. We map the sites of H3 cleavage and identify Cathepsin L as a protease responsible for proteolytically processing the N-terminal H3 tail. In addition, our data suggest that H3 cleavage may be regulated by covalent modifications present on the histone tail itself. Our studies underscore the intriguing possibility that histone proteolysis, brought about by Cathepsin L and potentially other family members, plays a role in development and differentiation that was not previously recognized.

Oral potentially malignant disorders - An assessment of knowledge and attitude to future education in undergraduate dental students.

INTRODUCTION: The aim of this study was to assess the knowledge and clinical experience of oral potentially malignant disorders (OPMDs) in undergraduate dental students in six European countries (Croatia, France, Italy, Portugal, Spain and United Kingdom) and assess student's attitude and preference to future education on the topic. A secondary aim was to identify gaps in student's knowledge and clinical practice. The study was a part of the Erasmus+ project "Oral Potentially Malignant Disorders: Healthcare Professionals Training" (Grant No: 2020-1-UK01-KA202-078917). MATERIALS AND METHODS: An online questionnaire was distributed to all final-year students in six partner universities. This consisted of four parts assessing: (1) knowledge on OPMDs, (2) clinical experience with this group of patients, (3) self-rated competence in the management of OPMDs and (4) preferences with regard to future education. RESULTS: Two hundred and sixty final-year dental students from six partner universities responded to the questionnaire. Response rates varied from 12% to 92% between partner universities. Significant differences in clinical experience and knowledge were found between students. Students with more clinical exposure to OPMDs rated their knowledge and competence in the management of OPMDs higher than students with less clinical experience. The majority of students were interested in future education on OPMDs, preferably via short educational videos. CONCLUSION: The majority of students have received theoretical knowledge of OPMDs during their undergraduate studies, however, not all had clinical exposure to this group of patients. Students were open to further education on OPMDs. Important deficiencies in knowledge were identified that need to be addressed and it is anticipated that the e-learning platform and e-book that are in development by partner institutions will help to improve overall knowledge of OPMDs.

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases.

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

Multistate models for the natural history of cancer progression.

BACKGROUND: Multistate models can be effectively used to characterise the natural history of cancer. Inference from such models has previously been useful for setting screening policies. METHODS: We introduce the basic elements of multistate models and the challenges of applying these models to cancer data. Through simulation studies, we examine (1) the impact of assuming time-homogeneous Markov transition intensities when the intensities depend on the time since entry to the current state (i.e., the process is time-inhomogenous semi-Markov) and (2) the effect on precancer risk estimation when observation times depend on an unmodelled intermediate disease state. RESULTS: In the settings we examined, we found that misspecifying a time-inhomogenous semi-Markov process as a time-homogeneous Markov process resulted in biased estimates of the mean sojourn times. When screen-detection of the intermediate disease leads to more frequent future screening assessments, there was minimal bias induced compared to when screen-detection of the intermediate disease leads to less frequent screening. CONCLUSIONS: Multistate models are useful for estimating parameters governing the process dynamics in cancer such as transition rates, sojourn time distributions, and absolute and relative risks. As with most statistical models, to avoid incorrect inference, care should be given to use the appropriate specifications and assumptions.

The illness-death model for family studies.

Family studies involve the selection of affected individuals from a disease registry who provide right-truncated ages of disease onset. Coarsened disease histories are then obtained from consenting family members, either through examining medical records, retrospective reporting, or clinical examination. Methods for dealing with such biased sampling schemes are available for continuous, binary, and failure time responses, but methods for more complex life history processes are less developed. We consider a simple joint model for clustered illness-death processes which we formulate to study covariate effects on the marginal intensity for disease onset and to study the within-family dependence in disease onset times. We construct likelihoods and composite likelihoods for family data obtained from biased sampling schemes. In settings where the disease is rare and data are insufficient to fit the model of interest, we show how auxiliary data can augment the composite likelihood to facilitate estimation. We apply the proposed methods to analyze data from a family study of psoriatic arthritis carried out at the University of Toronto Psoriatic Arthritis Registry.

Independence conditions and the analysis of life history studies with intermittent observation.

Multistate models provide a powerful framework for the analysis of life history processes when the goal is to characterize transition intensities, transition probabilities, state occupancy probabilities, and covariate effects thereon. Data on such processes are often only available at random visit times occurring over a finite period. We formulate a joint multistate model for the life history process, the recurrent visit process, and a random loss to follow-up time at which the visit process terminates. This joint model is helpful when discussing the independence conditions necessary to justify the use of standard likelihoods involving the life history model alone and provides a basis for analyses that accommodate dependence. We consider settings with disease-driven visits and routinely scheduled visits and develop likelihoods that accommodate partial information on the types of visits. Simulation studies suggest that suitably constructed joint models can yield consistent estimates of parameters of interest even under dependent visit processes, providing the models are correctly specified; identifiability and estimability issues are also discussed. An application is given to a cohort of individuals attending a rheumatology clinic where interest lies in progression of joint damage.

Isolated axial disease in psoriatic arthritis and ankylosing spondylitis with psoriasis.

OBJECTIVES: To compare isolated axial psoriatic arthritis (PsA), axial PsA with peripheral involvement and isolated axial ankylosing spondylitis (AS) with psoriasis. To evaluate predictors for developing peripheral disease from isolated axial PsA over time. METHODS: Two PsA and AS cohorts identified patients with PsA with axial disease and isolated axial patients with AS with psoriasis. Logistic regression compared isolated axial PsA to axial PsA with peripheral involvement and isolated axial AS with psoriasis. Cox proportional hazards model evaluated predictors for developing peripheral disease from isolated axial PsA. RESULTS: Of 1576 patients with PsA, 2.03% had isolated axial disease and 29.38% had axial and peripheral disease. human leucocyte antigen HLA-B*27 positivity (OR 25.00, 95% CI 3.03 to 206.11) and lower Health Assessment Questionnaire scores (OR 0.004, 95% CI 0.00 to 0.28) were associated with isolated axial disease. HLA-B*27 also predicted peripheral disease development over time (HR 7.54, 95% CI 1.79 to 31.77). Of 1688 patients with AS, 4.86% had isolated axial disease with psoriasis. Isolated axial patients with PsA were older at diagnosis (OR 1.06, 95% CI 1.01 to 1.13), more likely to have nail lesions (OR 12.37, 95% CI 2.22 to 69.07) and less likely to have inflammatory back pain (OR 0.12, 95% CI 0.02 to 0.61) compared with patients with isolated axial AS with psoriasis. CONCLUSIONS: Isolated axial PsA and AS with psoriasis are uncommon. HLA-B*27 positivity is associated with isolated axial PsA and may identify those who develop peripheral disease over time. Isolated axial PsA is associated with better functional status. Isolated axial PsA appears clinically distinct from isolated axial AS with psoriasis.