Clement Ma

The gene-sex association quality control (QC) metric was developed during the era of early small-sized genome-wide association studies and typically filter variants based on controls alone. While this practice had minimal impact in early studies, in contemporary large-scale settings they can introduce systematic bias by disproportionately discarding variants with pronounced sex differences in allele frequencies (AF), potentially removing real gene-sex interaction signals. To address this limitation, we introduce Sex-Prevalence Adjusted Allelic Difference Estimates (SPADE) metric, an X chromosome-inclusive QC framework that not only preserves potentially informative variants exhibiting gene-sex interaction but also enables a rapid and exploratory scan for such interaction. SPADE adjusts for sex-specific disease prevalence, maintains correct type I error control, and reduces the risk of falsely excluding variants compared to existing QC approaches. Extensive simulations across diverse disease architectures demonstrated that SPADE remained well calibrated, whereas the controls-only approach exhibited massively inflated type I error when variants were associated with disease. We further developed an open-source command-line software implementation to facilitate its application in large-scale genetic studies. Applying SPADE to an autism spectrum disorder (ASD) case-control cohort (6,873 cases and 8,981 controls) comprising of the Autism Speaks MSSNG, Simons Simplex Collection (SSC), and Simons Powering Autism Research (SPARK) datasets, we demonstrate that SPADE is well calibrated relative to a controls-only approach. Notably, the complementary exploratory gene-sex interaction scan implicates a sex antagonistic region encompassing RBMX2, SLC25A14, and BCORL1 (lead SNP rs150885581: A>G, p = 6.19 * 10 ^ -9), providing candidate genes for future functional investigation.

Precision medicine has given rise to a spectrum of biomarker-guided trial designs, from simple enrichment and strategy designs to more complex adaptive frameworks. To address the need for user-friendly tools that span this spectrum, we developed a unified R Shiny platform that first implements three standard designs: the randomize-all design, the enrichment design, and the biomarker-strategy design, allowing researchers to perform power and sample size calculations under each framework with intuitive inputs and visual outputs. Building on this foundation, the platform further extends to support two-stage general randomized basket trial designs with interim analysis, which can be viewed as a generalization of the standard designs to multiple biomarker-defined subgroups. The tool was rigorously validated by comparison with established R pipelines and published formulas, and user testing confirmed its intuitive interface. By providing seamless integration from standard to advanced designs under a common input–output framework, our platform enables researchers to directly compare power and sample size requirements across different design choices using the same underlying assumptions. The result is a freely accessible tool offering effective visualizations for the full spectrum of biomarker-guided trial designs, available at https://ampt.obicloud.ca/ . Future improvements may further expand the tool’s capabilities to accommodate the increasing complexity of trial designs needed by the research community.

Phase 2 clinical trials typically rely on a single primary endpoint, yet in many settings, treatment efficacy must be demonstrated across multiple co‐primary endpoints. Such settings require intersection‐union hypothesis testing, in which the global null hypothesis is rejected only when all individual component hypotheses are rejected. Existing phase 2 designs for binary endpoints are largely restricted to single‐arm or single‐endpoint settings, or rely on large‐sample normal approximations for two‐arm trials, which may be inadequate when sample sizes are small. We developed exact and simulation‐based methods for designing two‐arm, two‐stage phase 2 trials with two co‐primary binary endpoints using the bivariate binomial distribution, allowing different correlations between endpoints across treatment arms. The proposed framework identifies optimal designs that minimize the expected sample size and minimax designs that minimize the maximum sample size, while allowing early termination for futility at the first stage. For small sample sizes (approximately per arm), the exact method is computationally feasible and yields analytically exact operating characteristics under the assumed bivariate binomial distribution. For larger sample sizes, however, exact computations become intensive. To address this, we evaluated a simulation‐based approach and a normal approximation by comparing their performance against the exact method. The simulation approach selected designs that closely matched those from the exact method, whereas the normal approximation often deviated. Therefore, we recommend the simulation‐based method as a computationally efficient and accurate alternative for moderate‐to‐large sample size trials.

Background: Telephone-based Cognitive Behavioral Therapy (Tele-CBT) has shown to reduce disordered eating and psychological distress after metabolic and bariatric surgery (MBS). However, it is currently unknown how Tele-CBT impacts outcomes long term, and if differences in weight loss trajectories following Tele-CBT emerge with a long-term follow-up. This study aimed to identify whether Tele-CBT remains effective at 18 months post-intervention for improving psychological distress and maladaptive eating, and mitigating recurrent weight gain. Methods: This large, multisite randomized control trial was conducted at three hospital-based MBS programs in Ontario, Canada. Participants (n = 306) were randomized 1:1 to receive either Tele-CBT or standard MBS care. The primary outcome was percentage of total weight loss (%TWL). Secondary outcomes included disordered eating (Binge Eating Scale, Emotional Eating Scale) and psychological distress (Patient Health Questionnaire-9, Generalized Anxiety Disorder-7). Linear mixed models assessed group-by-time interactions across five time points: baseline (1-year post-MBS), post-intervention, and 3-, 12-, and 18-month follow-ups. Results: Tele-CBT resulted in significant post-intervention improvements in binge eating (MD = − 0.46, p < .001), emotional eating (MD = − 0.14, p = 0.01), anxiety (MD = − 0.40, p < .001), and depressive symptoms (MD = − 0.47, p < .001). These improvements were all sustained at 3 months post-intervention (p < .05) whereas only improvements for emotional eating were sustained at 12 months post-intervention (MD = − 0.15, p = 0.01). There were no significant differences in %TWL trajectories between the Tele-CBT and control groups. Conclusions: Tele-CBT provides psychological benefits, particularly in reducing emotional eating. Findings highlight the need for continued psychosocial support to sustain other psychological benefits and mitigate recurrent weight gain post-MBS and further research on optimizing intervention timing and duration. Trial Registration: ClinicalTrials.gov Identifier: NCT03315247.

Recent policy recommendations on the use of health data call for an understanding of privacy experiences and perspectives. A secondary analysis of a national survey was conducted to characterize public experiences views on digital health (n=2010). This study found that 69.8% of participants are unaware of health privacy laws yet 71.8% were confident in protecting their online privacy. These variables were significantly associated with beliefs that their privacy was adequately protected. This study reinforces the discourse that public engagement in building awareness of the law and confidence in data protection will be critical in fostering trust in health privacy safeguards.

PURPOSE: Novel therapies targeting specific genomic alterations are a promising treatment approach for relapsed/refractory cancer. Patients with specific alterations may be more likely to respond. Trial designs should maximize opportunities for such patients to enroll on these trials. Existing designs do not enrich for patients with specific alterations. We developed the adaptive Targeted Agent-Continual Reassessment Method (TARGET-CRM) to optimize dose finding and enrich for patients with specific alterations, and applied it in a pediatric phase I trial. METHODS: Patients were stratified to cohort A (unspecified tumors) or cohort B (rare genomic alterations). The TARGET-CRM design permits cohort B patients to immediately enroll at one dose level below the currently evaluated dose level instead of waiting for an open slot at the current dose level. Using simulations, we compared the operating characteristics (accuracy—the proportion of trials in which the true maximum tolerated dose [MTD] was identified/recommended; safety—the dose-limiting toxicity [DLT] rate; the proportion of cohort B patients enrolled) of the TARGET-CRM, standard CRM, and 3 + 3 designs across various scenarios. RESULTS: The proportion of enrolled patients who were cohort B was higher for TARGET-CRM (90%-100%) compared with CRM (approximately 85%) and 3 + 3 (approximately 79%). The DLT rate and rate the true MTD was recommended were similar for TARGET-CRM and CRM, differing by only 0%-4% and 0%-4%, respectively. Results were similar regardless of trial sample size and proportion of cohort B patients in the population. CONCLUSION: In phase I dose-finding trials of targeted agents, the Bayesian adaptive TARGET-CRM design maximizes enrollment of patients hypothesized as most likely to benefit from the targeted agent, while maintaining similar or superior accuracy and safety as the CRM and 3 + 3 designs.

Basket trials can efficiently evaluate a single treatment across multiple diseases with a common shared target. Prior methods for randomized basket trials required baskets to have the same sample and effect sizes. To that end, we developed a general randomized basket trial with an interim analysis (RaBIt) that allows for unequal sample sizes and effect sizes per basket. RaBIt is characterized by pruning at an interim stage and then analyzing a pooling of the remaining baskets. We derived the analytical power and type 1 error for the design. We first show that our results are consistent with the prior methods when the sample and effect sizes were the same across baskets. As we adjust the sample allocation between baskets, our threshold for the final test statistic becomes more stringent in order to maintain the same overall type 1 error. Finally, we notice that if we fix a sample size for the baskets proportional to their accrual rate, then at the cost of an almost negligible amount of power, the trial overall is expected to take substantially less time than the non-generalized version.

Basket trial designs with interim analysis have gained significant attention due to their adaptability, flexibility, and scalability. In response to the need for user-friendly tools that enhance the real-world applicability of these designs, we developed a web-based interface aimed at facilitating two-stage basket trial designs. Built using R Shiny, the tool was rigorously validated for output consistency by comparing it to an established R pipeline. Additionally, user testing was conducted to ensure the interface is intuitive and easy to use. The result is a freely accessible tool that provides effective and convenient visualizations for general basket trial designs with interim analysis, available at https://desmondzeyachen.shinyapps.io/AdaptiveTwoStageBasketTrialFeb14/ . Future improvements may further expand the tool’s capabilities to accommodate the increasing complexity of trial designs needed by the research community.

Background: As an established treatment for major depressive disorder (MDD), cognitive behavioral therapy (CBT) is now implemented and assessed in internet-based formats that, when combined with smartphone apps, enable secure text messaging. As an adjunct to such internet-based CBT (ICBT) approaches, text messaging has been associated with increased adherence and therapeutic alliance. Objective: This study analyzed data from the intervention arm of a randomized control trial evaluating 24-week ICBT for MDD (intervention arm) against standard-care psychiatry (waitlist control). The aim of this secondary analysis was to assess MDD symptom improvement in relation to the frequency and content of text messages sent by ICBT participants to Navigator-Coaches during randomized control trial participation. Higher text frequency in general and in 3 conceptual categories (appreciating alliance, alliance building disclosures, and agreement confirmation) was hypothesized to predict larger MDD symptom improvement. Methods: Participants were young adults (18-30 years) from the Centre for Addiction and Mental Health. The frequencies of categorized texts from 20 ICBT completers were analyzed with respect to MDD symptom improvement using linear regression models. Texts were coded by 2 independent coders and categorized using content analysis. MDD symptoms were measured using the Beck Depression Inventory-II (BDI-II). Results: Participants sent an average of 136 text messages. Analyses indicated that BDI-II improvement was negatively associated with text messaging frequency in general (β=–0.029, 95% CI –0.11 to 0.048) and in each of the 3 categories: appreciating alliance (β=–0.096, 95% CI –0.80 to 0.61), alliance building disclosures (β=–0.098, 95% CI –0.28 to 0.084), and agreement confirmation (β=–0.076, 95% CI –0.40 to 0.25). Altogether, the effect of text messaging on BDI-II improvement was uniformly negative across statistical models. More text messaging appeared associated with less MDD symptom improvement. Conclusions: The hypothesized positive associations between conceptually categorized text messages and MDD symptom improvement were not supported in this study. Instead, more text messaging appeared to indicate less treatment benefit. Future studies with larger samples are needed to discern the optimal use of text messaging in ICBT approaches using adjunctive modes of communication. Trial Registration: Clinical Trials.gov NCT03406052; https://www.clinicaltrials.gov/ct2/show/NCT03406052

Background: Patient portals are web-based systems through which patients can access their personal health information and communicate with their clinicians. The integration of patient portals into mental health care settings has been evolving over the past decade, as cumulated research to date has highlighted the potential role of portals in facilitating positive health outcomes. However, it is currently unknown whether portal use can foster interprofessional collaboration between clinicians and patients or whether the portal is a tool to support an already established collaborative relationship. Objective: This mixed methods study aimed to understand how the use of a patient portal within mental health settings can impact the level of interprofessional collaboration between clinicians and patients. Methods: This study was conducted in a large mental health care organization in Ontario, Canada. A convergent mixed methods design was used, where the primary data collection methods included questionnaires and semistructured interviews with patients who had experience using a portal for their mental health care. For the quantitative strand, participants completed the Health Care Communication Questionnaire and the Self-Empowerment subscale of the Mental Health Recovery Measure at 3 time points (baseline, 3 months of use, and 6 months of use) to measure changes in scores over time. For the qualitative strand, semistructured interviews were conducted at the 3-month time point to assess the elements of interprofessional collaboration associated with the portal. Results: For the quantitative strand, 113 participants completed the questionnaire. For the Health Care Communication Questionnaire scores, the raw means of the total scores at the 3 time points were as follows: baseline, 43.01 (SD 7.28); three months, 43.19 (SD 6.65); and 6 months, 42.74 (SD 6.84). In the univariate model with time as the only independent variable, the scores did not differ significantly across the 3 time points (P=.70). For the Mental Health Recovery Measure scores, the raw mean total scores at the 3 time points were as follows: baseline, 10.77 (SD 3.63); three months, 11.09 (SD 3.81); and 6 months, 11.10 (SD 3.33). In the univariate model with time as the only independent variable, the scores did not differ significantly across the 3 time points (P=.34). For the qualitative strand, 10 participants were interviewed and identified various elements of how interprofessional collaboration can be supplemented through the use of a patient portal, including improved team functioning, communication, and conflict resolution. Conclusions: Although the quantitative data produced nonsignificant findings in interprofessional collaboration scores over time, the patients’ narrative accounts described how the portal can support various interprofessional collaboration concepts, such as communication, leadership, and conflict resolution. This provides useful information for clinicians to support the interprofessional relationship when using a portal within a mental health setting. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2018-025508