Breastfeeding Duration and Cardiometabolic Health during Adolescence: A Longitudinal Analysis.

OBJECTIVE: To investigate the longitudinal association between breastfeeding duration and cardiometabolic health, using repeated measures study design among children and adolescents. STUDY DESIGN: This study included 634 offsprings aged 10 to 21 years (52% female) from the Early Life Exposure in Mexico to Environmental Toxicants birth cohort followed up to four time points during adolescence. Breastfeeding duration was prospectively quantified using questionnaires during early childhood. Cardiometabolic risk factors, body composition, and weight-related biomarkers were assessed as outcomes during adolescent follow-up visits. Sex-stratified linear mixed-effects models were used to model the association between quartiles of breastfeeding duration and outcomes, adjusting for age and additional covariates. RESULTS: Median breastfeeding duration was 7 months (minimum = 0, maximum = 36). Boys in the second quartile (median breastfeeding = 5 months) had lower total fat mass % (ß (SE) -3.2 (1.5) P = .037), and higher lean mass % (3.1 (1.6) P = .049) and skeletal muscle mass % (1.8 (0.8) P = .031) compared with the reference group (median breastfeeding = 2 months). A positive linear trend between breastfeeding duration and trunk lean mass % (0.1 (0.04) P = .035) was found among girls. No association was found with other cardiometabolic indicators. CONCLUSION: Despite sex-specific associations of breastfeeding duration with body composition, there was a lack of substantial evidence for the protective effects of breastfeeding against impaired cardiometabolic health during adolescence among Mexican youth. Further longitudinal studies with a robust assessment of breastfeeding are recommended.

MRS-assessed brain GABA modulation in response to task performance and learning.

Gamma-aminobutyric acid (GABA), the most important inhibitory neurotransmitter in the human brain, has long been considered essential in human behavior in general and learning in particular. GABA concentration can be quantified using magnetic resonance spectroscopy (MRS). Using this technique, numerous studies have reported associations between baseline GABA levels and various human behaviors. However, regional GABA concentration is not fixed and may exhibit rapid modulation as a function of environmental factors. Hence, quantification of GABA levels at several time points during the performance of tasks can provide insights into the dynamics of GABA levels in distinct brain regions. This review reports on findings from studies using repeated measures (n = 41) examining the dynamic modulation of GABA levels in humans in response to various interventions in the perceptual, motor, and cognitive domains to explore associations between GABA modulation and human behavior. GABA levels in a specific brain area may increase or decrease during task performance or as a function of learning, depending on its precise involvement in the process under investigation. Here, we summarize the available evidence and derive two overarching hypotheses regarding the role of GABA modulation in performance and learning. Firstly, training-induced increases in GABA levels appear to be associated with an improved ability to differentiate minor perceptual differences during perceptual learning. This observation gives rise to the 'GABA increase for better neural distinctiveness hypothesis'. Secondly, converging evidence suggests that reducing GABA levels may play a beneficial role in effectively filtering perceptual noise, enhancing motor learning, and improving performance in visuomotor tasks. Additionally, some studies suggest that the reduction of GABA levels is related to better working memory and successful reinforcement learning. These observations inspire the 'GABA decrease to boost learning hypothesis', which states that decreasing neural inhibition through a reduction of GABA in dedicated brain areas facilitates human learning. Additionally, modulation of GABA levels is also observed after short-term physical exercise. Future work should elucidate which specific circumstances induce robust GABA modulation to enhance neuroplasticity and boost performance.

Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods.

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

Effect of long working hours on psychological distress among young workers in different types of occupation.

BACKGROUND: We investigated how the association between long working hours and psychological distress varies across different employment and occupation types in young workers. METHODS: Examining a nationally representative sample of 7246 Korean workers (3621 women) aged 15 to 40, we analyzed 23,492 observations spanning from 2016 to 2020. Psychological distress was measured using the Brief Encounter Psychosocial Instrument. We employed a generalized estimating equation to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Of the total observations, 5.2% worked <35 h/week, 52.9% worked 35-40 h/week, 23.5% worked 41-48 h/week, 10.3% worked 49-54 h/week, and 8.2% worked ≥55 h/week. The OR (95% CI) of the association between long working hours and psychological distress was 1.38 (1.11-1.72) for <35 h/week, 1.47 (1.32-1.65) for 41-48 h/week, 1.74 (1.49-2.04) for 49-54 h/week, and 2.11 (1.75-2.55) for ≥55 h/week compared to 35-40 h/week. The OR (95% CI) of the association between working ≥55 h/week and psychological distress was significantly higher among wage workers (OR [95% CI]: 2.37 [1.94-2.89]) compared to self-employed workers (OR [95% CI]: 0.84 [0.52-1.36]). Additionally, the OR (95% CI) of the association between working ≥55 h/week and psychological distress was significantly higher among white-collar workers (OR [95% CI]: 3.24 [2.54-4.13]) compared to service/sales workers (OR [95% CI]: 1.22 [0.86-1.72]) or blue-collar workers (OR [95% CI]: 1.71 [1.10-2.67]). No clear gender differences were observed. CONCLUSION: Psychological distress caused by long working hours can be pronounced among white-collar and wage workers.

Association Between Temporary Employment and Current Smoking and Change in Smoking Behaviors: A Prospective Cohort Study From South Korea (2009-2018).

BACKGROUND: Previous studies have suggested that employment insecurity is associated with adverse health outcomes. We explored the association between temporary employment and smoking behaviors. METHODS: We analyzed 11,795 workers (51,867 observations) from the Korea Health Panel Study (2009-2018). Employment types were categorized as regular, fixed-term, or daily, based on the duration of labor contract. The outcomes were current smoking status and changes in smoking behavior (initiation or cessation) in the following year. Generalized estimating equations were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: The proportions of fixed-term and daily workers were 41.2% and 16.4% for women and 23.6% and 12.4% for men, respectively. Temporary employment was associated with increased odds of current smoking, while also demonstrating prospective associations with changes in smoking behaviors. For instance, in prospective analyses, male workers with fixed-term and daily employments were associated with a decreased likelihood of smoking cessation (OR 0.77; 95% CI, 0.65-0.91 for fixed-term employment and OR 0.66; 95% CI, 0.52-0.83 for daily employment) in the following year compared to those with regular employment. Moreover, those experiencing consecutive temporary employment was most inversely associated with smoking cessation in both men (OR 0.56; 95% CI, 0.44-0.71) and women (OR 0.37; 95% CI, 0.16-0.85) compared to those experiencing consecutive regular employment. However, no clear association between temporary employment and smoking initiation was observed in both men and women. CONCLUSION: Temporary employment is directly associated with current smoking and inversely associated with smoking cessation. Policies are needed to improve job insecurity among temporary employees.

Serial Sampling of the Small Airway Epithelium to Identify Persistent Smoking-dysregulated Genes.

Rationale: The small airway epithelium (beyond the sixth generation), the initiation site of smoking-induced airway disorders, is highly sensitive to the stress of smoking. Because of variations over time in smoking habits, the small airway epithelium transcriptome is dynamic, fluctuating not only among smokers but also within each smoker. Objectives: To perform accurate assessment of the smoking-related dysregulation of the human small airway epithelium despite the variation of smoking within the same individual and of the effects of smoking cessation on the dysregulated transcriptome. Methods: We conducted serial sampling of the same smokers and nonsmoker control subjects over time to identify persistent smoking dysregulation of the biology of the small airway epithelium over 1 year. We conducted serial sampling of smokers who quit smoking, before and after smoking cessation, to assess the effect of smoking cessation on the smoking-dysregulated genes. Measurements and Main Results: Repeated measures ANOVA of the small airway epithelium transcriptome sampled four times in the same individuals over 1 year enabled the identification of 475 persistent smoking-dysregulated genes. Most genes were normalized after 12 months of smoking cessation; however, 53 (11%) genes, including CYP1B1, PIR, ME1, and TRIM16, remained persistently abnormally expressed. Dysregulated pathways enriched with the nonreversible genes included xenobiotic metabolism signaling, bupropion degradation, and nicotine degradation. Conclusions: Analysis of repetitive sampling of the same individuals identified persistent smoking-induced dysregulation of the small airway epithelium transcriptome and the effect of smoking cessation. These results help identify targets for the development of therapies that can be applicable to smoking-related airway diseases.

Employment status and depressive symptoms in taiwanese older adults: an 11-year prospective cohort study.

BACKGROUND: Taiwan became an aged society in March 2018, and it is expected to become a super-aged society by 2025. The trend of increasing proportions of older adults continuing to work is inevitable. However, few studies have been conducted to investigate the effects of employment on the mental health of older adults. Therefore, we longitudinally explored the relationship between employment status and depressive symptoms in Taiwanese older adults. METHODS: The study included 5,131 individuals aged 50 and above, of which 55.6% were men, who had participated in the national-wide Taiwan Longitudinal Study of Aging in 1996, 1999, 2003, and 2007. Of them, 1,091 older adults had completed all four surveys. Depressive symptoms were assessed using the Center for Epidemiological Studies of Depression scale; the total score on this scale ranges from 0 to 30. Employment status was assessed during each survey wave. Logistic regression was performed using a cross-sectional design. The effects of unemployment on depressive symptoms were analyzed using a generalized estimating equation model with a repeated measures design. RESULTS: In each survey wave, employed older adults exhibited better mental health than did unemployed ones. After adjustments for potential confounders, unemployment was found to exert a significant adverse effect on depressive symptoms. The repeated measures analysis revealed that employment protected against depressive symptoms, as noted in the subsequent surveys conducted after 3 to 4 years (aOR [95% CI] = 0.679 [0.465-0.989]). CONCLUSION: Employment may reduce the risk of depressive symptoms in community-dwelling older adults in Taiwan.

Vaping habits and respiratory symptoms using a smartphone app platform.

BACKGROUND: Widespread use of e-cigarette (EC) or vaping products causes respiratory disorders including the nationwide outbreak of e-cigarette or vaping product use-associated lung injury (EVALI) in 2019. Chronic adverse health effects are now being reported as well. To address this important public health issue, an innovative approach of epidemic control and epidemiologic study is required. We aimed to assess the association between short-term and long-term use of EC products and respiratory health in adults using smartphone app data. METHODS: A population-based, repeated measures, longitudinal smartphone app study that performed 8-day survey participation over 60 days for each participant from August 2020 to March 2021, including 306 participants aged 21 years and older in the US. The participants were asked to complete the respiratory health questionnaire daily, weekly, and monthly on their smartphone app. We analyzed the association between vaping habits and respiratory health using generalized linear mixed models (GLMMs). RESULTS: EC use in the previous 7 days was associated with frequent cough (OR: 5.15, 95% CI: 2.18, 12.21), chronic cough (OR: 3.92, 95% CI: 1.62, 9.45), frequent phlegm (OR: 3.99, 95% CI: 1.44, 11.10), chronic phlegm (OR: 3.55, 95% CI: 1.41, 8.96), episodes of cough and phlegm (OR: 4.68, 95% CI: 1.94, 11.28), mMRC grade 3-4 dyspnea (OR: 3.32, 95% CI: 1.35 to 8.13), chest cold (OR: 3.07, 95% CI: 1.29, 7.33), eye irritation (OR: 2.94, 95% CI: 1.34, 6.47) and nose irritation (OR : 2.02, 95% CI: 0.95, 4.30). Relatively long-term effects of the past 90 days EC use was associated with an increased risk of wheeze (OR: 3.04, 95% CI: 1.31, 7.03), wheeze attack (OR: 2.78, 95% CI: 1.07, 7.24), mMRC grade 3-4 dyspnea (OR: 2.54, 9% CI: 1.05 to 6.18), eye irritation (OR: 3.16, 95% CI: 1.49, 6.68), and eye irritation during the past month (OR: 3.50, 95% CI: 1.52, 8.04). CONCLUSIONS: In this smartphone app-based repeated measures study, short-term and relatively long-term use of EC increased the risk of respiratory symptoms.

Identifying latent classes of physical activity profiles over time among adolescents in Ontario, Canada.

BACKGROUND: Physical activity behaviours are known to be highly correlated. Adolescents who participate in one type of physical activity (e.g., physical education) have a greater likelihood of participating in other physical activities (e.g., organized sports); however, little research has examined participation rates in various physical activity behaviours concurrently. This study identified longitudinal physical activity profiles among secondary school aged youth in Ontario, Canada. METHODS: We used data from the COMPASS Study, a school-based prospective cohort study of adolescents in Canada. Using a repeated measures latent class analysis, Ontario students who participated in grade 9 PE in 2015-16 were analysed through to 2018-19 (n = 1,917). Latent classes were defined by: PE participation, guideline adherence (≥ 60 min/day of moderate to vigorous activity over the last 7 days), and sport participation (varsity, community, and/or intramural). Multinomial logistic regression models were used to examine associations between latent class membership and student characteristics. RESULTS: Three distinct latent classes were identified for females and four were identified for males. These classes were: (1) Guidelines (high probability of guideline adherence; females: 44%; males: 16%), (2) PE & Sports (high probability of PE and sport participation; females: 33%; males: 43%), (3) Guidelines & Sports (high probability of guideline adherence and sport participation; females: 23%; males: 23%;), and (4) Inactive (low probability of all physical activity indicators; males: 18%). Strength training, sleep, and English grade were associated with class membership among females. Ethno-racial identity, weekly spending money, strength training, and English and math grades were associated with class membership among males. CONCLUSIONS: Findings suggest that latent physical activity profiles differ by sex. Guideline adherence was the most common class among females, indicating high levels of independent physical activity, whereas PE & Sport participation was the most common class among males, indicating greater tendency towards organized activities. Additionally, a substantial number of male students were not engaging in any physical activity. Participation in both PE and sports did not necessarily lead to meeting physical activity guidelines, highlighting that these activities alone may not be providing sufficient levels of physical activity that align with current recommendations for Canadian youth.

Complex interplay of heavy metals and renal injury: New perspectives from longitudinal epidemiological evidence.

BACKGROUND: Epidemiological studies have reported associations between heavy metals and renal function. However, longitudinal studies are required to further validate these associations and explore the interactive effects of heavy metals on renal function and their directional influence. METHOD: This study, conducted in Northeast China from 2016 to 2021, included a four-time repeated measures design involving 384 participants (1536 observations). Urinary concentrations of chromium (Cr), cadmium (Cd), manganese (Mn), and lead (Pb) were measured, along with renal biomarkers including urinary microalbumin (umAlb), urinary albumin-to-creatinine ratio (UACR), N-acetyl-ß-D-glucosaminidase (NAG), and ß2-microglobulin (ß2-MG) levels. Estimated glomerular filtration rate (eGFR) was calculated. A Linear Mixed Effects Model (LME) examined the association between individual metal exposure and renal biomarkers. Subsequently, Quantile g-computation and Bayesian Kernel Machine Regression (BKMR) models assessed the overall effects of heavy metal mixtures. Marginal Effect models examined the directional impact of metal interactions in the BKMR on renal function. RESULT: Results indicate significant impacts of individual and combined exposures of Cr, Cd, Pb, and Mn on renal biomarkers. Metal interactions in the BKMR model were observed, with synergistic effects of Cd-Cr on NAG, umAlb, UACR; Cd-Pb on NAG, UACR; Pb-Cr on umAlb, UACR, eGFR-MDRD, eGFR-EPI; and an antagonistic effect of Mn-Pb-Cr on UACR. CONCLUSION: Both individual and combined exposures to heavy metals are associated with renal biomarkers, with significant synergistic interactions leading to renal damage. Our findings elucidate potential interactions among these metals, offering valuable insights into the mechanisms linking multiple metal exposures to renal injury.

Patterns of antidepressant prescribing and health-related outcomes among older adults in Northern Ireland: an administrative data study.

OBJECTIVES: This study identifies patterns of antidepressant prescribing and subsequent hospital admissions from 2010 to 2018 amongst older adults in Northern Ireland (NI). METHOD: Participants comprised all General Practitioner (GP)-registered adults aged fifty-five years and above on 01/01/2010 (n = 386,119). Administrative data linkage included demographic information; antidepressant prescribing data from the NI Enhanced Prescribing Database (EPD); and hospital patient admissions. Repeated measures latent class analysis (RMLCA) identified patterns of antidepressant prescribing (from 2010 to 2018). RESULTS: RMLCA identified four latent classes: decreasing antidepressant prescribing (5.9%); increasing antidepressant prescribing (8.0%); no-antidepressant prescribing (68.7%); and long-term antidepressant prescribing (17.5%). Compared with those in no-antidepressant prescribing class, persons in the remaining classes were more likely to be female and younger, and less likely to live in either rural areas or less-deprived areas. Compared with no-antidepressant prescribing, those with increasing antidepressant prescribing were 60% and 52% more likely to be admitted to hospital in 2019 and 2020, respectively, and their admission rate per year was 11% and 8% higher in 2019 and 2020, respectively. Similarly, those with long-term prescriptions were 70% and 67% more likely to be admitted to hospital in 2019 and 2020, respectively, and their admission rate per year was 14% and 9% higher in 2019 and 2020, respectively. CONCLUSION: Findings show that approximately 26% of the NI hospital admissions population were impacted by sustained or increasing antidepressant prescribing. Because of their increased likelihood of hospitalization, these individuals may benefit from psychosocial support and social prescribing alternatives to psychopharmacological treatment.

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis.

BACKGROUND: The assessment of the effectiveness of teaching interventions in enhancing students' understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students' understanding of the PCNE Classification System in pharmaceutical education. METHODS: Undergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires. RESULTS: The paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire's validity. CONCLUSION: The implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students' comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

Evaluation of a flexible piecewise linear mixed-effects model in the analysis of randomized cross-over trials.

Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross-over designs over conventional parallel designs is increased precision, thanks to within-subject comparisons. In the statistical literature, more recent developments are discussed in the analysis of cross-over trials, in particular regarding repeated measures. A piecewise linear model within the framework of mixed effects has been proposed in the analysis of cross-over trials. In this article, we report on a simulation study comparing performance of a piecewise linear mixed-effects (PLME) model against two commonly cited models-Grizzle's mixed-effects (GME) and Jones & Kenward's mixed-effects (JKME) models-used in the analysis of cross-over trials. Our simulation study tried to mirror real-life situation by deriving true underlying parameters from empirical data. The findings from real-life data confirmed the original hypothesis that high-dose iodine salt have significantly lowering effect on diastolic blood pressure (DBP). We further sought to evaluate the performance of PLME model against GME and JKME models, within univariate modeling framework through a simulation study mimicking a 2 × 2 cross-over design. The fixed-effects, random-effects and residual error parameters used in the simulation process were estimated from DBP data, using a PLME model. The initial results with full specification of random intercept and slope(s), showed that the univariate PLME model performed better than the GME and JKME models in estimation of variance-covariance matrix (G) governing the random effects, allowing satisfactory model convergence during estimation. When a hierarchical view-point is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive-definite. The PLME model is preferred especially in modeling an increased number of random effects, compared to the GME and JKME models that work equally well with random intercepts only. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters.

Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation.

The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on- and off-treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article introduces a novel approach to parameterising this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a retrieved dropout compliance model, using both on- and off-treatment data, to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable, or are poorly estimated leading to unrealistically large standard errors in the resulting analysis. We refer to this new approach as retrieved dropout reference-base centred multiple imputation.

Kinect-Based Gait Analysis System Design and Concurrent Validity in Persons with Anterolateral Shoulder Pain Syndrome, Results from a Pilot Study.

As part of an investigation to detect asymmetries in gait patterns in persons with shoulder injuries, the goal of the present study was to design and validate a Kinect-based motion capture system that would enable the extraction of joint kinematics curves during gait and to compare them with the data obtained through a commercial motion capture system. The study included eight male and two female participants, all diagnosed with anterolateral shoulder pain syndrome in their right upper extremity with a minimum 18 months of disorder evolution. The participants had an average age of 31.8 ± 9.8 years, a height of 173 ± 18 cm, and a weight of 81 ± 15 kg. The gait kinematics were sampled simultaneously with the new system and the Clinical 3DMA system. Shoulder, elbow, hip, and knee kinematics were compared between systems for the pathological and non-pathological sides using repeated measures ANOVA and 1D statistical parametric mapping. For most variables, no significant difference was found between systems. Evidence of a significant difference between the newly developed system and the commercial system was found for knee flexion-extension (p < 0.004, between 60 and 80% of the gait cycle), and for shoulder abduction-adduction. The good concurrent validity of the new Kinect-based motion analysis system found in this study opens promising perspectives for clinical motion tracking using an affordable and simple system.

CITIES: Clinical trials with intercurrent events simulator.

Although clinical trials are often designed with randomization and well-controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real-life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data-generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real-life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real-life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real-life trials.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 × $\times$ 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi et al. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a 2 × 2 $2\times 2$ crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modeling framework.

Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials.

INTRODUCTION: Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods. METHODS: We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clinical trial data that mimic or modify the Clinical Dementia Rating Sum of Boxes progression trajectories observed in the Clarity AD lecanemab trial. RESULTS: Our study demonstrates that the proposed methods can generate more precise estimates by considering two crucial factors: (1) the absolute difference between treatment arms, and (2) the observed progression rate in the treatment arm. DISCUSSION: Quantifying treatment effects as time savings in disease progression offers distinct advantages. To provide comprehensive estimations, it is important to use various methods. HIGHLIGHTS: We explore the statistical considerations of the current method for estimating time savings. We proposed alternative methods that provide time savings estimations based on the observed absolute differences. By using various methods, a more comprehensive estimation of time savings can be achieved.

A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases.

Ordinal data in a repeated measures design of a crossover study for rare diseases usually do not allow for the use of standard parametric methods, and hence, nonparametric methods should be considered instead. However, only limited simulation studies in settings with small sample sizes exist. Therefore, starting from an Epidermolysis Bullosa simplex trial with the above-mentioned design, a rank-based approach using the R package nparLD and different generalized pairwise comparisons (GPC) methods were compared impartially in a simulation study. The results revealed that there was not one single best method for this particular design, because a trade-off exists between achieving high power, accounting for period effects, and for missing data. Specifically, nparLD as well as the unmatched GPC approaches do not address crossover aspects, and the univariate GPC variants partly ignore the longitudinal information. The matched GPC approaches, on the other hand, take the crossover effect into account in the sense of incorporating the within-subject association. Overall, the prioritized unmatched GPC method achieved the highest power in the simulation scenarios, although this may be due to the specified prioritization. The rank-based approach yielded good power even at a sample size of N = 6 $N=6$ , whereas the matched GPC method could not control the type I error.

Item response theory for before-after designs in interprofessional education research.

Although Item Response Theory (IRT) has been recommended for helping advance interprofessional education (IPE) research, its use remains limited. This may be partly explained by potential misconceptions regarding IRT`s "limitation" to cross-sectional data. The aim of this study is to demonstrate how Item Response Theory (IRT) can be applied effectively in before-and-after designs in IPE research. Specifically, a two-week before-after design with survey methodology using the Extended Professional Identity Scale (EPIS), an interprofessional identity measure, was conducted among n = 146 mixed health-science students. Results indicated that EPIS increased significantly before-after intervention by .74 standardised mean differences, t146 = 7.73, p < .05. The before-after IRT model also gave a test-retest reliability estimate of .60 which was considered acceptable. Comparison of the IRT model with a conventional paired-t-test indicated similar effect size estimates of Cohen's d = .56 and .54, respectively. We demonstrate IRT`s flexibility to before-after studies in IPE. Application of this model can yield accurate changes in target IPE constructs, and it is advantageous to classical test theory vis-à-vis baseline differences.