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Background Longitudinal data can be used to study disease progression and are often collected at irregular intervals. When the assessment times are informative about the severity of the disease, regression analyses of the outcome trajectory over time based on Generalized Estimating Equations (GEEs) result in biased estimates of regression coefficients. Inverse-intensity weighted GEEs (IIW-GEEs) are a popular approach to account for informative assessment times and yield unbiased estimates of outcome model coefficients when the assessment times and outcomes are conditionally independent given previously observed data. However, a consequence of irregular assessment times is that some patients may have no follow-up assessments at all, and it is common practice to omit these patients from analyses when studying the outcome trajectory over time. Methods We show mathematically that IIW-GEEs yield biased estimates of regression coefficients when patients with no follow-up assessments are excluded from analyses. We design a simulation study to evaluate how the bias varies with sample size, assessment frequency, follow-up time, and the informativeness of the assessment time process. Using the STAR*D trial of treatments for major depressive disorder, we examine the extent of bias in practice. Results Our simulation results showed the bias incurred by omitting patients with no follow-up visits increased as visit frequency decreased and as the duration of follow-up decreased. In the STAR*D trial, omitting patients with no follow-up visits led to over-estimation of the rate of improvement in depressive symptoms. Conclusions Studies should be designed to ensure patients with no follow-up are included in the data. This can be achieved by a) creating inception cohorts; b) when taking sub-samples of existing cohorts, ensuring that patients without follow-up assessments are included; c) dropping exclusion criteria based on availability of follow-up visits.

Background When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a conditional treatment effect. Model-based standardization is typically applied to average the model predictions over the target covariate distribution, and generate a covariate-adjusted estimate of the marginal treatment effect. Methods The standard approach to model-based standardization involves maximum-likelihood estimation and use of the non-parametric bootstrap. We introduce a novel, general-purpose, model-based standardization method based on multiple imputation that is easily applicable when the outcome model is a generalized linear model. We term our proposed approach multiple imputation marginalization (MIM). MIM consists of two main stages: the generation of synthetic datasets and their analysis. MIM accommodates a Bayesian statistical framework, which naturally allows for the principled propagation of uncertainty, integrates the analysis into a probabilistic framework, and allows for the incorporation of prior evidence. Results We conduct a simulation study to benchmark the finite-sample performance of MIM in conjunction with a parametric outcome model. The simulations provide proof-of-principle in scenarios with binary outcomes, continuous-valued covariates, a logistic outcome model and the marginal log odds ratio as the target effect measure. When parametric modeling assumptions hold, MIM yields unbiased estimation in the target covariate distribution, valid coverage rates, and similar precision and efficiency than the standard approach to model-based standardization. Conclusion We demonstrate that multiple imputation can be used to marginalize over a target covariate distribution, providing appropriate inference with a correctly specified parametric outcome model and offering statistical performance comparable to that of the standard approach to model-based standardization.

Bronchiolitis exerts a high burden on children, their families and the healthcare system. The Canadian Bronchiolitis Epinephrine Steroid Trial (CanBEST) assessed whether administering epinephrine alone, dexamethasone alone, or in combination (EpiDex) could reduce bronchiolitis-related hospitalizations among children less than 12 months of age compared to placebo. CanBEST demonstrated a statistically significant reduction in 7-day hospitalization risk with EpiDex in an unadjusted analysis but not after adjustment. To explore the probability that EpiDex results in a reduction in hospitalizations using Bayesian methods. Using prior distributions that represent varying levels of preexisting enthusiasm or skepticism, i.e., how confident or doubtful one is that EpiDex may reduce hospitalizations, and information about the treatment effect before data were collected, the posterior distribution of the relative risk of hospitalization compared to placebo was determined. The probability that the treatment effect is less than 1, 0.9, 0.8 and 0.6, indicating increasing reductions in hospitalization risk, are computed alongside 95% credible intervals. Combining a minimally informative prior distribution with the data from CanBEST provides comparable results to the original analysis. Unless strongly skeptical views about the effectiveness of EpiDex were considered, the 95% credible interval for the treatment effect lies below 1, indicating a reduction in hospitalizations. There is a 90% probability that EpiDex results in a clinically meaningful reduction in hospitalization of 10% even when incorporating skeptical views, with a 67% probability when considering strongly skeptical views. A Bayesian analysis demonstrates a high chance that EpiDex reduces hospitalization rates for bronchiolitis, although strongly skeptical individuals may require additional evidence to change practice. Clinical Trial registry name, registration number: Current Controlled Trials number, ISRCTN56745572.

Background Laceration repairs are a common, yet distressing procedure in children. While a range of strategies is used to treat this distress, there is currently no standard of care. The Anxiolysis for Laceration Repair in Children (ALICE) trial aims to identify the most effective pharmacological agent to manage laceration repair-associated distress. This paper outlines the statistical analysis plan for the ALICE trial.  The ALICE trial is a phase III, Bayesian, open-label trial that will identify the optimal agent for reducing distress among intranasal dexmedetomidine (IND), intranasal midazolam (INM), and inhaled nitrous oxide (N 2 O). The primary outcome, distress, will be measured by the Observational Scale of Behavioural Distress – Revised (OSBD-R). Scores from the OSBD-R will be analyzed using a Bayesian mixed effects model with data-driven prior distributions. Samples from the model’s posterior distributions will be used to calculate the probability of being best statistic (P best ), which will effectively rank the interventions. The trial will also evaluate delayed maladaptive behaviours, need for additional physical restraint, adverse events, and need for additional sedation as secondary outcomes. Furthermore, the trial will determine the costs associated with achieving adequate sedation in each treatment arm. Discussion This statistical analysis plan specifies the outcomes and analyses for the ALICE trial. The ALICE trial will provide evidence for the most effective agent for reducing distress in children receiving laceration repairs. Trial registration ClinicalTrials.gov NCT05383495 . Registered on May 16, 2022. 

Background Adaptive designs (ADs) are intended to make clinical trials more flexible, offering efficiency and potentially cost-saving benefits. Despite a large number of statistical methods in the literature on different adaptations to trials, the characteristics, advantages and limitations of such designs remain unfamiliar to large parts of the clinical and research community. This systematic review provides an overview of the use of ADs in published clinical trials (Part I). A follow-up (Part II) will compare the application of AD in trials in adult and pediatric studies, to provide real-world examples and recommendations for the child health community. Methods Published studies from 2010 to April 2020 were searched in the following databases: MEDLINE (Ovid), Embase (Ovid), and International Pharmaceutical Abstracts (Ovid). Clinical trial protocols, reports, and a secondary analyses using AD were included. We excluded trial registrations and interventions other than drugs or vaccines to align with regulatory guidance. Data from the published literature on study characteristics, types of adaptations, statistical analysis, stopping boundaries, logistical challenges, operational considerations and ethical considerations were extracted and summarized herein. Results Out of 23,886 retrieved studies, 317 publications of adaptive trials, 267 (84.2%) trial reports, and 50 (15.8%) study protocols), were included. The most frequent disease was oncology (168/317, 53%). Most trials included only adult participants (265, 83.9%),16 trials (5.4%) were limited to only children and 28 (8.9%) were for both children and adults, 8 trials did not report the ages of the included populations. Some studies reported using more than one adaptation (there were 390 reported adaptations in 317 clinical trial reports). Most trials were early in drug development (phase I, II (276/317, 87%). Dose-finding designs were used in the highest proportion of the included trials (121/317, 38.2 %). Adaptive randomization (53/317, 16.7%), with drop-the-losers (or pick-the-winner) designs specifically reported in 29 trials (9.1%) and seamless phase 2-3 design was reported in 27 trials (8.5%). Continual reassessment methods (60/317, 18.9%) and group sequential design (47/317, 14.8%) were also reported. Approximately two-thirds of trials used frequentist statistical methods (203/309, 64%), while Bayesian methods were reported in 24% (75/309) of included trials. Conclusion This review provides a comprehensive report of methodological features in adaptive clinical trials reported between 2010 and 2020. Adaptation details were not uniformly reported, creating limitations in interpretation and generalizability. Nevertheless, implementation of existing reporting guidelines on ADs and the development of novel educational strategies that address the scientific, operational challenges and ethical considerations can help in the clinical trial community to decide on when and how to implement ADs in clinical trials. Study protocol registration https://doi.org/10.1186/s13063-018-2934-7 .

Lacerations are the most common traumatic reason for children to visit an emergency department (ED), accounting for almost half of all procedures performed. Children experience considerable distress during laceration repair, despite routine application of local anesthetic. Pharmacologic anxiolysis may mitigate the negative practice of forcefully restraining a child, however, evidence for the most effective agent is lacking. We aim to determine the most effective anxiolytic agent for laceration repair in children. This is a multicentre, phase III, three-arm, adaptive, randomized, open-label, trial. We will include children 2-12 years with a single laceration requiring suture repair in the ED. Participants will be randomized to receive intranasal dexmedetomidine (IND) 3 mcg/kg, intranasal midazolam (INM) 0.4 mg/kg, or inhaled 50% nitrous oxide (N2O). The primary outcome is the weighted mean anxiolysis score using the Observational Scale of Behavioral Distress - Revised (OSBD-R) from initial positioning to tying of the last suture. Secondary outcomes include need for additional anxiolytic, need for physical restraint, adverse events (AEs), and delayed maladaptive behaviors. The primary analysis will be conducted by intention-to-treat. Results will report posterior means, standard deviations (SDs), and 95% high density posterior credible intervals for Total Distress Score on the OSBD-R. We will rank interventions based on the probability that an intervention is superior (Pbest) and the Surface Area Under the Cumulative Ranking Curve (SUCRA) to indicate relative anxiolytic efficacy. The mean difference in Total Distress Score and secondary outcomes will be estimated using Bayesian models. Ethics approval will be obtained from institutional review boards of the participating sites. Informed consent will be obtained from guardians of all participants in addition to assent from all participants. Study data will be submitted for publication. Clinicaltrials.gov NCT05383495.

Bipolar disorder is a highly heritable psychiatric condition for which specific genetic factors remain largely unknown. In the present study, we used combined whole-exome sequencing and linkage analysis to identify risk loci and dissect the contribution of common and rare variants in families with a high density of illness. Overall, 117 participants from 15 Australian extended families with bipolar disorder (72 with affective disorder, including 50 with bipolar disorder type I or II, 13 with schizoaffective disorder–manic type and 9 with recurrent unipolar disorder) underwent whole-exome sequencing. We performed genome-wide linkage analysis using MERLIN and conditional linkage analysis using LAMP. We assessed the contribution of potentially functional rare variants using a gene-based segregation test. We identified a significant linkage peak on chromosome 10q11-q21 (maximal single nucleotide polymorphism = rs10761725; exponential logarithm of the odds [LODexp] = 3.03; empirical p = 0.046). The linkage interval spanned 36 protein-coding genes, including a gene associated with bipolar disorder, ankyrin 3 (ANK3). Conditional linkage analysis showed that common ANK3 risk variants previously identified in genome-wide association studies — or variants in linkage disequilibrium with those variants — did not explain the linkage signal (rs10994397 LOD = 0.63; rs9804190 LOD = 0.04). A family-based segregation test with 34 rare variants from 14 genes under the linkage interval suggested rare variant contributions of 3 brain-expressed genes: NRBF2 (p = 0.005), PCDH15 (p = 0.002) and ANK3 (p = 0.014). We did not examine non-coding variants, but they may explain the remaining linkage signal. Combining family-based linkage analysis with next-generation sequencing data is effective for identifying putative disease genes and specific risk variants in complex disorders. We identified rare missense variants in ANK3, PCDH15 and NRBF2 that could confer disease risk, providing valuable targets for functional characterization.

Background Utilizing Bayesian methods in clinical trials has become increasingly popular, as they can incorporate prior information into the design, and allow for smaller sample sizes while providing reliable and robust statistical results. Various Bayesian methods for sample size determination are available, and while these methods are well justified and understood, it is unclear how they are being used in practice. This study aims to understand how sample sizes for Bayesian efficacy randomized clinical trials (RCTs) are determined and inform future designs of Bayesian trials. Methods A systematic literature review was conducted in May 2023 and updated in July 2025. We included completed RCTs which (a) assessed the efficacy of interventions in humans; (b) utilized a Bayesian framework for the primary data analysis; (c) published in English; and (d) enrolled participants between December 2009 – July 2025. Results The literature search produced 74,833 records, of which 27,890 were duplicates, and 46,943 were screened using manual and automated screening. 283 full texts were screened and 164 studies moved to extraction. Our findings demonstrate a slow increase in RCTs using Bayesian methods to analyse primary efficacy data from 2012 onwards, with a sharp increase during the COVID-19 pandemic (42%). The most common method for sample size determination in Bayesian RCTs was a hybrid approach (58%) in which elements of Bayesian and frequentist theory are combined. Bayesian RCTs predominantly took place in North America (34%) and mainly focused on adult study populations (85%). Bayesian trials were used in a variety of disease areas; the most common being COVID-19 (31%). Conclusion Fully Bayesian methods for sample size determination are rarely used in practice, despite significant theoretical development. Our review revealed a lack of standardized reporting across Bayesian RCTs, making it challenging to review the sample size determination. The CONSORT statement indicates that RCTs must report sample size calculations; adhered to by only 84% of included RCTs. Among RCTs that reported sample size determination, relevant information was frequently omitted from reports and discussed in poorly structured supplementary materials. Thus, there is a critical need for greater transparency, standardization and translation of relevant methodology in Bayesian RCTs.

The contactin-associated protein-like 2 (CNTNAP2) gene is a member of the neurexin superfamily. CNTNAP2 was first implicated in the cortical dysplasia-focal epilepsy (CDFE) syndrome, a recessive disease characterized by intellectual disability, epilepsy, language impairments and autistic features. Associated SNPs and heterozygous deletions in CNTNAP2 were subsequently reported in autism, schizophrenia and other psychiatric or neurological disorders. We aimed to comprehensively examine evidence for the role of CNTNAP2 in susceptibility to psychiatric disorders, by the analysis of multiple classes of genetic variation in large genomic datasets. In this study we used: i) summary statistics from the Psychiatric Genomics Consortium (PGC) GWAS for seven psychiatric disorders; ii) examined all reported CNTNAP2 structural variants in patients and controls; iii) performed cross-disorder analysis of functional or previously associated SNPs; and iv) conducted burden tests for pathogenic rare variants using sequencing data (4,483 ASD and 6,135 schizophrenia cases, and 13,042 controls). The distribution of CNVs across CNTNAP2 in psychiatric cases from previous reports was no different from controls of the database of genomic variants. Gene-based association testing did not implicate common variants in autism, schizophrenia or other psychiatric phenotypes. The association of proposed functional SNPs rs7794745 and rs2710102, reported to influence brain connectivity, was not replicated; nor did predicted functional SNPs yield significant results in meta-analysis across psychiatric disorders at either SNP-level or gene-level. Disrupting CNTNAP2 rare variant burden was not higher in autism or schizophrenia compared to controls. Finally, in a CNV mircroarray study of an extended bipolar disorder family with 5 affected relatives we previously identified a 131kb deletion in CNTNAP2 intron 1, removing a FOXP2 transcription factor binding site. Quantitative-PCR validation and segregation analysis of this CNV revealed imperfect segregation with BD. This large comprehensive study indicates that CNTNAP2 may not be a robust risk gene for psychiatric phenotypes.

Background Adaptive clinical trials increasingly aim to detect heterogeneity of treatment effect (HTE) to guide personalized care. However, most adaptive designs rely on predefined subgroups and are limited in their ability to uncover unknown or complex sources of HTE. Bayesian statistical methods offer a flexible alternative, enabling real-time learning and adaptation within trials. This review evaluates Bayesian methods used to detect hidden HTE in adaptive clinical trials, with attention to their methodological innovations, operating characteristics, and consideration of equity and inclusion in trial design. Methods We conducted a systematic search of MEDLINE, Embase, and other databases to identify original studies that developed Bayesian methods for detecting unknown HTE within adaptive clinical trial designs. Eligible studies were reviewed and synthesized based on design features, statistical methodology, operating characteristics, reproducibility, and whether equity-related factors were explicitly considered. Equity considerations included whether studies incorporated variables related to underrepresented populations—such as age, sex, race/ethnicity, or geography—examined intersectional subgroup effects, or explicitly framed their methods as tools to address health disparities. Results Of 2826 screened records, seven studies met inclusion criteria. Bayesian methods included random partition models, spatial models, logistic regression with dimension reduction, adaptive randomization using machine learning classifiers, and adaptive enrichment or platform designs incorporating model averaging or latent subgroup estimation. In simulation studies, these methods often showed improvements in subgroup detection, efficiency, or power relative to non-Bayesian comparators. None were tested using real-world trial data. Reproducibility was limited overall, with analytic code only available for the three most recent studies. Notably, none explicitly framed their methods as tools to address inequities in treatment outcomes across population subgroups. Conclusions The small number of simulation-based studies illustrates preliminary but promising directions for applying Bayesian methods to detect HTE in adaptive clinical trials. While these approaches demonstrate potential to enhance trial adaptability, scalability, and inclusiveness, current evidence remains limited and largely conceptual. Incorporating an equity lens into future methodological development, alongside greater emphasis on empirical validation and open science practices, will be essential to determine their practical value in advancing equitable clinical research.