Resting-state EEG predicts cognitive decline in a neuropathologically diagnosed longitudinal community autopsied cohort.

OBJECTIVE: To assess correlative strengths of quantitative electroencephalography (qEEG) and visual rating scale EEG features on cognitive outcomes in only autopsied cases from the Arizona Study of Neurodegenerative Disorders (AZSAND). We hypothesized that autopsy proven Parkinson Disease will show distinct EEG features from Alzheimer's Disease prior to dementia (mild cognitive impairment). BACKGROUND: Cognitive decline is debilitating across neurodegenerative diseases. Resting-state EEG analysis, including spectral power across frequency bins (qEEG), has shown significant associations with neurodegenerative disease classification and cognitive status, with autopsy confirmed diagnosis relatively lacking. METHODS: Biannual EEG was analyzed from autopsied cases in AZSAND who had at least one rsEEG (>1 min eyes closed±eyes open). Analysis included global relative spectral power and a previously described visual rating scale (VRS). Linear mixed regression was performed for neuropsychological assessment and testing within 2 years of death (n = 236, 594 EEG exams) in a mixed linear regression model. RESULTS: The cohort included cases with final clinicopathologic diagnoses of Parkinson's disease (n = 73), Alzheimer disease (n = 65), and tauopathy not otherwise specified (n = 56). A VRS score of 3 diffuse or frequent generalized slowing) over the study duration was associated with an increase in consensus diagnosis cognitive worsening at 4.9 (3.1) years (HR 2.02, CI 1.05-3.87). Increases in global theta power% and VRS were the most consistently associated with large regression coefficients inversely with cognitive performance measures. CONCLUSION: Resting-state EEG analysis was meaningfully related to cognitive performance measures in a community-based autopsy cohort. EEG deserves further study and use as a cognitive biomarker.

Longitudinal Outcomes Following Mitral Valve Repair for Infective Endocarditis.

Mitral valve repair is the ideal approach in managing mitral valve infective endocarditis for patients requiring surgery. However, viable repair is influenced by the extent of valve destruction and there can be technical challenges in reconstruction following debridement. Overall, data describing long-term outcomes following mitral repair of infective endocarditis are scarce. We, therefore, assessed the late outcomes of 101 consecutive patients who underwent mitral valve repair for IE at the University of Ottawa Heart Institute from 2001 to 2021. The 5- and 10-year survival rate was 80.8 ± 4.7% and 61.2 ± 9.2%, respectively. Among these 101 patients, 7 ultimately required mitral valve reoperation at a median of 5 years after their initial operation. These patients were of a mean age of 35.9 ± 7.3 years (range 22-44 years) at the time of their initial operation. The 5- and 10-year freedom from mitral valve reoperation was 93.6 ± 3.4% and 87.7 ± 5.2%, respectively. Overall, mitral valve repair can be an effective method for treating infective endocarditis with a favourable freedom from reoperation and mortality over the long term.

Sex differences in the prospective association of excessively long reaction times and hazardous cannabis use at six months.

Objective: The neurocognitive risk mechanisms predicting divergent outcomes likely differ between men and women who use cannabis recreationally. Increasingly, the use of descriptive distributions including the ex-Gaussian has been applied to draw stronger inferences about neurocognitive health in clinical populations. The current project examines whether the long tail of reaction times (RTs) in a distribution, as characterized by the ex-Gaussian parameter tau which may represent difficulty with the regulation of arousal, predicts problematic cannabis use 6 months later in those who use cannabis recreationally, and whether sex moderates these prospective associations. Method: Young adults (ages 18-30, mean age 20.5 years, N =159, 57.2% women, 69.2% Caucasian) who recreationally used cannabis either occasionally (at least once per month) or frequently (three times or more per week) completed the Stroop Color-Word Task at baseline. Ex-Gaussian parameter tau was estimated for each participant. Self-report of hazardous cannabis use (CUDIT-R) and dysregulation of negative (DERS) and positive emotions (DERS-Positive) were obtained at baseline and 6-month follow-up. Results: For those with larger tau at baseline, being a man (but not a woman) was associated with increased difficulty regulating positive emotions concurrently (b = -0.01, F (1,159) = 5.48, p = 0.02), and with hazardous cannabis use six months later (b = -0.007, F (1,159) = 4.42, p = 0.037) after controlling for baseline hazardous cannabis use. Conclusions: Excessively long RTs during cognitive performance may help characterize men at risk for increased hazardous use, which contributes to understanding between-sex heterogeneity in pathways towards cannabis use disorders.

Bayesian transition models for ordinal longitudinal outcomes.

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

An additive-multiplicative model for longitudinal data with informative observation times.

Designed clinical studies often assess outcomes at pre-planned time points. In most situations, standard statistical models, such as generalized linear mixed models and generalized additive models, are sufficient to depict the temporal trends of the outcome and produce valid inference. Complicating factors, however, do exist in practical data analyses. One complication arises when the outcome and observational processes are interdependent, that is, the observational process is informative; another challenge is patient characteristics may influence the longitudinally observed outcomes in non-additive ways, for example, by multiplicative factors. In this research, we extend the standard longitudinal models to accommodate informative observation through a more flexible modeling structure-one with additive-multiplicative components that do not require explicit specification of the dependency structure between the outcome and observation processes. Along this vein, we provide the essential theory for inference in such models. Simulation studies showed the proposed method performs well for finite-sample scenarios, and the method was applied to analyze a motivating example from an alcohol-associated hepatitis observational study.

The Effect of Chronic Altitude Exposure on COPD Outcomes in the SPIROMICS Cohort.

RATIONALE: Individuals with COPD have airflow obstruction and maldistribution of ventilation. For those living at high altitude, any gas exchange abnormality is compounded by reduced partial pressures of inspired oxygen. OBJECTIVES: Does residence at higher-altitude exposure affect COPD outcomes, including lung function, imaging characteristics, symptoms, health status, functional exercise capacity, exacerbations, or mortality? METHODS: From the SPIROMICS cohort, we identified individuals with COPD living below 1,000 ft (305 m) elevation (n= 1,367) versus above 4,000 ft (1,219 m) elevation (n= 288). Multivariable regression models were used to evaluate associations of exposure to high altitude with COPD-related outcomes. MEASUREMENTS AND MAIN RESULTS: Living at higher altitude was associated with reduced functional exercise capacity as defined by 6MWD (-32.3 m, (-55.7 to -28.6)). There were no differences in patient-reported outcomes as defined by symptoms (CAT, mMRC), or health status (SGRQ). Higher altitude was not associated with a different rate of FEV1 decline. Higher altitude was associated with lower odds of severe exacerbations (IRR 0.65, (0.46 to 0.90)). There were no differences in small airway disease, air trapping, or emphysema. In longitudinal analyses, higher altitude was associated with increased mortality (HR 1.25, (1.0 to 1.55)); however, this association was no longer significant when accounting for air pollution. CONCLUSIONS: Chronic altitude exposure is associated with reduced functional exercise capacity in individuals with COPD, but this did not translate into differences in symptoms or health status. Additionally, chronic high-altitude exposure did not affect progression of disease as defined by longitudinal changes in spirometry.

A Bayesian joint model for multivariate longitudinal and time-to-event data with application to ALL maintenance studies.

The most common type of cancer diagnosed among children is the Acute Lymphocytic Leukemia (ALL). A study was conducted by Tata Translational Cancer Research Center (TTCRC) Kolkata, in which 236 children (diagnosed as ALL patients) were treated for the first two years (approximately) with two standard drugs (6MP and MTx) and were then followed nearly for the next 3 years. The goal is to identify the longitudinal biomarkers that are associated with time-to-relapse, and also to assess the effectiveness of the drugs. We develop a Bayesian joint model in which a linear mixed model is used to jointly model three biomarkers (i.e. white blood cell count, neutrophil count, and platelet count) and a semi-parametric proportional hazards model is used to model the time-to-relapse. Our proposed joint model can assess the effects of different covariates on the progression of the biomarkers, and the effects of the biomarkers (and the covariates) on time-to-relapse. In addition, the proposed joint model can impute the missing longitudinal biomarkers efficiently. Our analysis shows that the white blood cell (WBC) count is not associated with time-to-relapse, but the neutrophil count and the platelet count are significantly associated with it. We also infer that a lower dose of 6MP and a higher dose of MTx jointly result in a lower relapse probability in the follow-up period. Interestingly, we find that relapse probability is the lowest for the patients classified into the "high-risk" group at presentation. The effectiveness of the proposed joint model is assessed through the extensive simulation studies.

Modeling multiple correlated end-organ disease trajectories: A tutorial for multistate and joint models with applications in diabetes complications.

State-of-the-art biostatistics methods allow for the simultaneous modeling of several correlated non-fatal disease processes over time, but there is no clear guidance on the optimal analysis in most settings. An example occurs in diabetes, where it is not known with certainty how microvascular complications of the eyes, kidneys, and nerves co-develop over time. In this article, we propose and contrast two general model frameworks for studying complications (sequential state and parallel trajectory frameworks) and review multivariate methods for their analysis, focusing on multistate and joint modeling. We illustrate these methods in a tutorial format using the long-term follow-up from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study public data repository. A formal comparison of prediction error and discrimination is included. Multistate models are particularly advantageous for determining the order and timing of complications, but require discretization of the longitudinal outcomes and possibly a very complex state space process. Intermittent observation of the states must be accounted for, and discretization is a probable disadvantage in this setting. In contrast, joint models can account for variations of continuous biomarkers over time and are particularly designed for modeling complex association structures between the complications and for performing dynamic predictions of an outcome of interest to inform clinical decisions (eg, a late-stage complication). We found that both models have helpful features that can better-inform our understanding of the complex trajectories that complications may take and can therefore help with decision making for patients presenting with diabetes complications.

Longitudinal Follow-Up of Children With HLHS and Association Between Norwood Shunt Type and Long-Term Outcomes: The SVR III Study.

OBJECTIVE: In the SVR trial (Single Ventricle Reconstruction), newborns with hypoplastic left heart syndrome were randomly assigned to receive a modified Blalock-Taussig-Thomas shunt (mBTTS) or a right ventricle-to-pulmonary artery shunt (RVPAS) at Norwood operation. Transplant-free survival was superior in the RVPAS group at 1 year, but no longer differed by treatment group at 6 years; both treatment groups had accumulated important morbidities. In the third follow-up of this cohort (SVRIII [Long-Term Outcomes of Children With Hypoplastic Left Heart Syndrome and the Impact of Norwood Shunt Type]), we measured longitudinal outcomes and their risk factors through 12 years of age. METHODS: Annual medical history was collected through record review and telephone interviews. Cardiac magnetic resonance imaging (CMR), echocardiogram, and cycle ergometry cardiopulmonary exercise tests were performed at 10 through 14 years of age among participants with Fontan physiology. Differences in transplant-free survival and complication rates (eg, arrhythmias or protein-losing enteropathy) were identified through 12 years of age. The primary study outcome was right ventricular ejection fraction (RVEF) by CMR, and primary analyses were according to shunt type received. Multivariable linear and Cox regression models were created for RVEF by CMR and post-Fontan transplant-free survival. RESULTS: Among 549 participants enrolled in SVR, 237 of 313 (76%; 60.7% male) transplant-free survivors (mBTTS, 105 of 147; RVPAS, 129 of 161; both, 3 of 5) participated in SVRIII. RVEF by CMR was similar in the shunt groups (RVPAS, 51±9.6 [n=90], and mBTTS, 52±7.4 [n=75]; P=0.43). The RVPAS and mBTTS groups did not differ in transplant-free survival by 12 years of age (163 of 277 [59%] versus 144 of 267 [54%], respectively; P=0.11), percentage predicted peak Vo2 for age and sex (74±18% [n=91] versus 72±18% [n=84]; P=0.71), or percentage predicted work rate for size and sex (65±20% versus 64±19%; P=0.65). The RVPAS versus mBTTS group had a higher cumulative incidence of protein-losing enteropathy (5% versus 2%; P=0.04) and of catheter interventions (14 versus 10 per 100 patient-years; P=0.01), but had similar rates of other complications. CONCLUSIONS: By 12 years after the Norwood operation, shunt type has minimal association with RVEF, peak Vo2, complication rates, and transplant-free survival. RVEF is preserved among the subgroup of survivors who underwent CMR assessment. Low transplant-free survival, poor exercise performance, and accruing morbidities highlight the need for innovative strategies to improve long-term outcomes in patients with hypoplastic left heart syndrome. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT0245531.

Bayesian predictive model averaging approach to joint longitudinal-survival modeling: Application to an immuno-oncology clinical trial.

In immuno-oncology clinical trials, multiple immunological biomarkers are usually examined over time to comprehensively and appropriately evaluate the efficacy of treatments. Because predicting patients' future survival statuses on the basis of such recorded longitudinal information might be of great interest, joint modeling of longitudinal and time-to-event data has been intensively discussed as a toolkit to implement such a prediction. To achieve a desirable predictive performance, averaging over multiple candidate predictive models to account for the model uncertainty might be a more suitable statistical approach than selecting the single best model. Although Bayesian model averaging can be one of the approaches, several problems related to model weights with marginal likelihoods have been discussed. To address these problems, we here propose a Bayesian predictive model averaging (BPMA) method that uses Bayesian leave-one-out cross-validation predictive densities to account for the subject-specific and time-dependent nature of the prediction. We examine the operating characteristics of the proposed BPMA method in terms of the predictive accuracy (ie, the calibration and discrimination abilities) in extensive simulation studies. In addition, we discuss the strengths and limitations of the proposed method by applying it to an immuno-oncology clinical trial in patients with advanced ovarian cancer.

Transversus abdominis release with biosynthetic mesh for large ventral hernia repair: a 5-year analysis of clinical outcomes and quality of life.

INTRODUCTION: Transversus abdominis release (TAR) may provide an optimal plane for mesh placement for large ventral hernias requiring medial myofascial flap advancement. Long-term outcomes of TAR for large ventral hernia repair (VHR) remains under-studied. This study aims to assess longitudinal clinical outcomes and quality of life (QoL) following large VHR with TAR and resorbable biosynthetic mesh. METHODS: Retrospective review of clinical outcomes and prospective QoL was performed for patients undergoing VHR with poly-4-hydroxybutyrate mesh and TAR from 2016 to 2021. Patients with ≤ 24 months of follow-up, defects ≤ 150 cm2, and parastomal hernias were excluded. Cost-related data was collected for each patient's hospital course. QoL was compared using paired Wilcoxon signed-rank tests. RESULTS: Twenty-nine patients met inclusion criteria. Median age and BMI were 61 years (53.2-68.1 years) and 31.4 kg/m2 (26.1-35.3 kg/m2). Average hernia defect was 390cm2 ± 152.9 cm2. All patients underwent previous abdominal surgery and were primarily Ventral Hernia Working Group 2 (58.6%). Two hernia recurrences (6.9%) occurred over the median follow-up period of 63.1 months (IQR 43.7-71.3 months), with no cases of mesh infection or explantation. Delayed healing and seroma occurred in 27 and 10.3% of patients, respectively. QoL analysis identified a significant improvement in postoperative QoL (p < 0.005), that continued throughout the 5-year follow-up period, with a 41% overall improvement. Cost analysis identified the hospital revenue generated was approximately equal to the direct costs of patient care. Higher costs were associated with ASA class and length of stay (p < 0.05). CONCLUSION: Large VHR with resorbable biosynthetic mesh and TAR can be performed safely, with a low recurrence and complication rate, acceptable hospital costs, and significant improvement in disease-specific QoL at long-term follow-up.

Joint Modeling of Longitudinal Outcome and Competing Risks: Application to HIV/AIDS Data.

BACKGROUND: Tuberculosis (TB) and human immunodeficiency virus (HIV) are major public health challenges globally, and the number of TB infections and death caused by HIV are high because of HIV/ TB co-infection. On the other hand, CD4 count plays a significant role in TB/HIV co-infections. We used a joint model of longitudinal outcomes and competing risks to identify the potential risk factors and the effect of CD4 cells on TB infection and death caused by HIV in HIV-infected patients. STUDY DESIGN: This was a retrospective cohort study. METHODS: The current study was performed on 1436 HIV+patients referred to Behavioral Diseases Counseling Centers in Kermanshah Province during 1998-2019. In this study, joint modeling was used to identify the effect of potential risk factors and CD4 cells on TB and death caused by HIV. RESULTS: The results demonstrated that the decreasing CD4 cell count was significantly associated with an increased risk of death, while it had no significant relation with the risk of TB. In addition, patients with TB were at a higher risk of death. Based on the results, a significant relationship was found between CD4 count and sex, marital status, education level, antiretroviral therapy (ART), time, and the interaction between time and ART. Further, people infected with HIV through sexual relationships were at higher risk of TB, while those with a history of imprisonment who received ART or were infected with HIV through drug injection had a lower risk of TB. CONCLUSION: The findings revealed that the decreasing CD4 count had a significant association with an increased risk of death caused by HIV. However, it was not significantly related to the risk of TB. Finally, patients with TB were at higher risk of death caused by HIV.

A threshold longitudinal Tobit quantile regression model for identification of treatment-sensitive subgroups based on interval-bounded longitudinal measurements and a continuous covariate.

Identification of a subgroup of patients who may be sensitive to a specific treatment is an important problem in precision medicine. This article considers the case where the treatment effect is assessed by longitudinal measurements, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. Recently, a linear mixed threshold regression model has been proposed but it assumes the longitudinal measurements are normally distributed. In many applications, longitudinal measurements, such as quality of life data obtained from answers to questions on a Likert scale, may be restricted in a fixed interval because of the floor and ceiling effects and, therefore, may be skewed. In this article, a threshold longitudinal Tobit quantile regression model is proposed and a computational approach based on alternating direction method of multipliers algorithm is developed for the estimation of parameters in the model. In addition, a random weighting method is employed to estimate the variances of the parameter estimators. The proposed procedures are evaluated through simulation studies and applications to the data from clinical trials.

Longitudinal urgency outcomes following robotic-assisted laparoscopic prostatectomy.

PURPOSE: Stress urinary incontinence (SUI) is a well-known adverse outcome following robotic-assisted laparoscopic prostatectomy (RALP). Although postoperative SUI has been extensively studied, little focus has been placed on understanding the natural history and impact of urgency symptoms following RALP. The UVA prostatectomy functional outcomes program (PFOP) was developed to comprehensively assess and optimize continence outcomes following RALP. The present study focuses on assessing urgency outcomes in this cohort. METHODS: PFOP patients with a minimum of 6-months follow up following RALP were included. The PFOP includes prospectively assessed incontinence and quality of life outcomes utilizing ICIQ-MLUTS, Urgency Perception Score (UPS), and IIQ-7 questionnaires. The primary study outcome was urgency urinary incontinence (UUI) as determined by ICIQ-MLUTS UUI domain. Secondary outcomes included urgency (UPS score) and quality of life (IIQ-7). RESULTS: Forty patients were included with median age 63.5 years. Fourteen (35%) patients reported UUI at baseline. UUI and QOL scores worsened compared to baseline at all time-points. Urgency worsened at 3-weeks and 3-months but returned to baseline by 6-months. Notably, 63% of patients without baseline UUI reported de-novo UUI at 6 months. Although QOL was lower in patients with versus without UUI (IIQ-7 score 3.0 vs 0.0, p = 0.009), severity of UUI was not associated with QOL when controlling for SUI severity. CONCLUSION: Our data demonstrate significantly worsened UUI from baseline and a large incidence of de-novo UUI following RALP. Further study is needed to inform how urgency and UUI and its treatment affect health-related quality of life following RALP.

A computational musculoskeletal arm model for assessing muscle dysfunction in chronic obstructive pulmonary disease.

Computational models have been used extensively to assess diseases and disabilities effects on musculoskeletal system dysfunction. In the current study, we developed a two degree-of-freedom subject-specific second-order task-specific arm model for characterizing upper-extremity function (UEF) to assess muscle dysfunction due to chronic obstructive pulmonary disease (COPD). Older adults (65 years or older) with and without COPD and healthy young control participants (18 to 30 years) were recruited. First, we evaluated the musculoskeletal arm model using electromyography (EMG) data. Second, we compared the computational musculoskeletal arm model parameters along with EMG-based time lag and kinematics parameters (such as elbow angular velocity) between participants. The developed model showed strong cross-correlation with EMG data for biceps (0.905, 0.915) and moderate cross-correlation for triceps (0.717, 0.672) within both fast and normal pace tasks among older adults with COPD. We also showed that parameters obtained from the musculoskeletal model were significantly different between COPD and healthy participants. On average, higher effect sizes were achieved for parameters obtained from the musculoskeletal model, especially for co-contraction measures (effect size = 1.650 ± 0.606, p < 0.001), which was the only parameter that showed significant differences between all pairwise comparisons across the three groups. These findings suggest that studying the muscle performance and co-contraction, may provide better information regarding neuromuscular deficiencies compared to kinematics data. The presented model has potential for assessing functional capacity and studying longitudinal outcomes in COPD.

A Bayesian joint model for multivariate longitudinal and time-to-event data with application to ALL maintenance studies.

The most common type of cancer diagnosed among children is the acute lymphocytic leukemia (ALL). A study was conducted by Tata Translational Cancer Research Center (TTCRC) Kolkata, in which 236 children (diagnosed as ALL patients) were treated for the first two years (approximately) with two standard drugs (6MP and MTx) and were then followed nearly for the next three years. The goal is to identify the longitudinal biomarkers that are associated with time-to-relapse, and also to assess the effectiveness of the drugs. We develop a Bayesian joint model in which a linear mixed model is used to jointly model three biomarkers (i.e. white blood cell count, neutrophil count, and platelet count) and a semi-parametric proportional hazards model is used to model the time-to-relapse. Our proposed joint model can assess the effects of different covariates on the progression of the biomarkers, and the effects of the biomarkers (and the covariates) on time-to-relapse. In addition, the proposed joint model can impute the missing longitudinal biomarkers efficiently. Our analysis shows that the white blood cell (WBC) count is not associated with time-to-relapse, but the neutrophil count and the platelet count are significantly associated with it. We also infer that a lower dose of 6MP and a higher dose of MTx jointly result in a lower relapse probability in the follow-up period. Interestingly, we find that relapse probability is the lowest for the patients classified into the "high-risk" group at presentation. The effectiveness of the proposed joint model is assessed through the extensive simulation studies.

Child-therapist interaction features impact Autism treatment response trajectories.

BACKGROUND: Identifying mechanisms of change in Autism treatment may help explain response variability and maximize efficacy. For this, the child-therapist interaction could have a key role as stressed by developmental models of intervention, but still remains under-investigated. AIMS: The longitudinal study of treatment response trajectories considering both baseline and child-therapist interaction features by means of predictive modeling. METHODS AND PROCEDURES: N = 25 preschool children were monitored for one year during Naturalistic Developmental Behavioral Intervention. N = 100 video-recorded sessions were annotated with an observational coding system at four time points, to extract quantitative interaction features. OUTCOMES AND RESULTS: Baseline and interaction variables were combined to predict response trajectories at one year, and achieved the best predictive performance. The baseline developmental gap, therapist's efficacy in child engagement, respecting children's timing after fast behavioral synchronization, and modulating the interplay to prevent child withdrawal emerged as key factors. Further, changes in interaction patterns in the early phase of the intervention were predictive of the overall response to treatment. CONCLUSIONS AND IMPLICATIONS: Clinical implications are discussed, stressing the importance of promoting emotional self-regulation during intervention and the possible relevance of the first period of intervention for later response.

The Longitudinal Effects of Second Step Child Protection Unit on Children: Gender as a Moderator.

We investigated the longitudinal effects of the Second Step Child Protection Unit (CPU; Committee for Children) on student outcomes through a randomized controlled trial. Eight schools with a total sample including 2,031 students were assigned randomly to the CPU intervention or the wait-list control condition. We employed a multi-process latent growth model using a structural equation modeling framework which simultaneously analyzed student outcome growth via the effects of the intervention. The moderating effect of gender was also included. Over four data collection waves (pretest, posttest, follow-up [6 months] and follow-up [12-months]), the intervention group students were better able to recognize appropriate requests in CSA scenarios than control group students over time. Teacher-student relations improved for the students in the intervention schools but worsened for the students in the control schools over time. Despite concerns that CSA prevention programs may result in the unintended consequence of making children more fearful, children in both conditions did not show increased fears over time. The longitudinal effect on CSA recognition was more pronounced among girls, who suffer from higher CSA prevalence, compared with boys. Implications for prevention, policy, and directions for future research are discussed.

Longitudinal analysis of shoulder arthroplasty utilization, clinical outcomes, and value: a comparative assessment of changes in improvement over 15 years with a single platform shoulder prosthesis.

BACKGROUND: The goal of this longitudinal analysis of anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA) utilization from 2007 to 2021 is to quantify changes in clinical outcomes, cost, and value, resulting from the introduction and adoption of new shoulder arthroplasty (SA) technologies. METHODS: We analyzed an international database of a single SA prosthesis (Equinoxe; Exactech, Inc.; Gainesville, FL, USA) for all clinical sites that have continuously enrolled cases from 2007 to 2021 to compare changes in primary aTSA and primary rTSA utilization and outcomes across 3, 5-year cohorts based upon the date of implantation. A value analysis was conducted across the 5-year implantation cohorts, with value measured by the ratio of each postoperative outcome measure at 24-36 months and 36-60 months after surgery, and the average implant selling price each year for the U.S. sites in constant 2007 U.S. dollars. These measures of value were compared between cohorts to quantify the impact of new technology introduced over the study period. RESULTS: A dramatic increase in rTSA utilization was observed across the 6 sites over the 15-year study period, along with a rapid adoption of new aTSA and rTSA technologies. The average patient receiving primary aTSA and primary rTSA changed over the 15-year study period, with significant shifts in diagnosis, comorbidities, and preoperative functional status. A comparison of postoperative results demonstrated that both aTSA and rTSA clinical and radiographic outcomes showed improvement relative to 2007-2011. Over this 15-year study period, the average aTSA implant selling price has been relatively stable while the average rTSA implant selling price has significantly declined. As a result, the value associated with the Equinoxe rTSA significantly increased for nearly every outcome measure at 24-36 months and 36-60 months after surgery, while value associated with the Equinoxe aTSA stayed relatively constant from 2007 to 2021. CONCLUSION: Our 6042-patient longitudinal analysis quantified numerous changes in utilization, outcomes, and value across 6 clinical sites over the 15-year study period. Rapid adoption of new aTSA and rTSA technologies was observed and clinical and radiographic outcomes improved relative to 2007-2011. These clinical improvements, in combination with steady aTSA and declining rTSA implant prices, have driven rTSA value to continuously increase while aTSA value has been maintained at a high-level over the 15-year study period with this particular SA system, even when considering the cost and adoption of new technologies.

Bayesian Design of Clinical Trials Using Joint Cure Rate Models for Longitudinal and Time-to-Event Data.

For clinical trial design and analysis, there has been extensive work related to using joint models for longitudinal and time-to-event data without a cure fraction (i.e., when all patients are at risk for the event of interest), but comparatively little treatment has been given to design and analysis of clinical trials using joint models that incorporate a cure fraction. In this paper, we develop a Bayesian clinical trial design methodology focused on evaluating the treatment's effect on a time-to-event endpoint using a promotion time cure rate model, where the longitudinal process is incorporated into the hazard model for the promotion times. A piecewise linear hazard model for the period after assessment of the longitudinal measure ends is proposed as an alternative to extrapolating the longitudinal trajectory. This may be advantageous in scenarios where the period of time from the end of longitudinal measurements until the end of observation is substantial. Inference for the time-to-event endpoint is based on a novel estimand which combines the treatment's effect on the probability of cure and its effect on the promotion time distribution, mediated by the longitudinal outcome. We propose an approach for sample size determination such that the design has a high power and a well-controlled type I error rate with both operating characteristics defined from a Bayesian perspective. We demonstrate the methodology by designing a breast cancer clinical trial with a primary time-to-event endpoint where longitudinal outcomes are measured periodically during follow up.