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Background: It remains uncertain whether the utilization of methylprednisolone during surgery effectively mitigates the occurrence of adverse outcomes. To examine the association between perioperative methylprednisolone administration and postoperative pleural effusion and pneumonia in older patients with non-small cell lung cancer. Methods: A retrospective cohort study included non-small cell lung cancer patients aged 65 years or older undergoing thoracic surgery between January 2012 and December 2019 in China. Primary outcome was pleural effusion. Secondary outcome was postoperative pneumonia. Multivariate logistic regression models assessed the independent effects of various factors on pleural effusion and pneumonia. Propensity score matching (PSM) method reduced selection bias enhancing causal inference validity. Subgroup analyses identified potential effect heterogeneity in specific sub-populations. Results: A total of 1951 older patients with non-small cell lung cancer were included. The incidence of postoperative pleural effusion in the methylprednisolone group before and after PSM matching was significantly lower than that in the control group (before PSM: 9.4% vs. 19.2, P < 0.001; after PSM: 9.8% vs. 18.2%, P < 0.001). There was no statistically significant difference in the incidence of postoperative pneumonia between the two groups before and after matching. After adjusting all the variables and PSM, we found that intraoperative methylprednisolone was associated with a reduction in postoperative pleural effusion in older patients with non-small cell lung cancer [odds ratio (OR) = 0.48, P < 0.001; OR = 0.47, P < 0.001]. Perioperative methylprednisolone showed consistent protective effects in all sub-populations of gender, age, surgery duration, and smoking (P all < 0.05). Logistic regression models and PSM found that methylprednisolone was not associated with postoperative pneumonia and long-term survival in older patients with non-small cell lung cancer. Conclusion: Perioperative methylprednisolone was associated with reducing the occurrence of postoperative pleural effusions in older patients with non-small volume lung cancer, but it was not associated with pneumonia or long-term survival outcomes. [ABSTRACT FROM AUTHOR]

Purpose: In light of COVID-19, telepractice for speech therapy has been increasingly adopted. Telepractice promotes accessibility to therapy services for those in rural environments, lowers the frequency of missed appointments, and reduces the costs of rehabilitation. The efficacy of telepractice has been scarcely explored in the aphasia literature. Preliminary research has demonstrated comparable results of telepractice and in-person therapy for people with aphasia, but the current scope of research is insufficient to guide clinical practice. The present study examined whether the virtual administration of a picturenaming therapy paradigm was as effective as in-person administration. Method: The treatment effects of two similar clinical trials, one completed inperson (n = 13) and one completed virtually (n = 13), are compared. Participants were adults with chronic (> 6 months) poststroke aphasia. Both clinical trials administered Cued Picture-Naming Therapy 4 days a week for 2 weeks (eight treatment sessions). Treatment outcomes were analyzed using Tau-U effect sizes and Mann--Whitney U tests. Results: Weighted Tau-U averages showed an advantage of telepractice over in-person treatment in the acquisition effects of trained words, with participants demonstrating a very large effect (0.84, p < .01) following telepractice and a large effect (0.75, p < .01) following in-person treatment. Both telepractice and in-person rehabilitation demonstrated significant treatment effects and were not significantly different from each other per Mann--Whitney U independentsamples t tests. Conclusions: The present study demonstrated that telepractice of a picturenaming paradigm is as effective as in-person treatment administration. This justifies the use of telepractice to overcome accessibility and cost barriers to speech therapy administration and justifies taking patient preference into account. Future research should explore the efficacy of telepractice for treatments that promote greater generalizability to functional communication. Supplemental Material: https://doi.org/10.23641/asha.27641031 [ABSTRACT FROM AUTHOR]

Context Despite recent advances in antidepressants in treating major depression (MDD), their usage is marred by adverse effects and social stigmas. Probiotics may be an efficacious adjunct or standalone treatment, potentially circumventing the aforementioned issues with antidepressants. However, there is a lack of head-to-head clinical trials between these 2 interventions. Objective A systematic review and network meta-analysis was conducted to compare the efficacy and acceptability of these 2 interventions in treating MDD. Data Sources Six databases and registry platforms for the clinical trial were systematically searched to identify the eligible double-blinded, randomized controlled trials published between 2015 and 2022. Data Exaction Two authors selected independently the placebo-controlled trials of antidepressants and microbiota-targeted interventions (prebiotics, probiotics, and synbiotics) used for the treatment of MDD in adults (≥18 years old). Standardized mean differences (SMDs) of depressive symptom scores from individual trials were pooled for network meta-analysis (PROSPERO no. CRD42020222305). Results Forty-two eligible trials covering 22 interventions were identified, of which 16 were found to be effective in MDD treatment and the certainty of evidence was moderate to very low. When all trials were considered, compared with placebo, SMDs of interventions ranged from −0.16 (95% credible interval: −0.30, −0.04) for venlafaxine to −0.81 (−1.06, −0.52) for escitalopram. Probiotics were superior to brexpiprazole (SMD [95% credible interval]: −0.42 [−0.68, −0.17]), cariprazine (−0.44 [−0.69, −0.24]), citalopram (−0.37 [−0.66, −0.07]), duloxetine (−0.26, [−0.51, −0.04]), desvenlafaxine (−0.38 [−0.63, −0.14]), ketamine (−0.32 [−0.66, −0.01]), venlafaxine (−0.47 [−0.73, −0.23]), vilazodone (−0.37 [−0.61, −0.12]), vortioxetine (−0.39 [−0.63, −0.15]), and placebo (−0.62 [−0.86, −0.42]), and were noninferior to other antidepressants. In addition, probiotics ranked the second highest in the treatment hierarchy after escitalopram. Long-term treatment (≥8 weeks) using probiotics showed the same tolerability as antidepressants. Conclusion Probiotics, compared with antidepressants and placebo, may be efficacious as an adjunct or standalone therapy for treating MDD. Systematic Review Registration PROSPERO registration no. CRD42020222305. [ABSTRACT FROM AUTHOR]

Objective: To assess the efficacy of pelvic floor muscle training and physical therapy interventions in patients with low back pain. Data sources: The Ovid–Medline, Ovid–Embase, Cochrane Library, CINAHL, Web of Science, and PEDro databases were searched for randomised, controlled trials published in English or Korean between database inception and September 2024. Review Methods: Studies providing pelvic floor muscle training in individuals with low back pain were included. The risk of bias using the Cochrane Risk of Bias 2 tool and the grading of recommendation, assessment, development, and evaluation (GRADE) system was used to evaluate the quality of evidence. The meta-analysis was performed using Review Manager software 5.4. Results: Nineteen studies were included in this review. Pelvic floor muscle training showed low certainty evidence for improving pain (standardised mean difference = −0.73, 95% CI [−1.10, −0.36]) and reflected a clinically meaningful reduction in pain. The evidence for disability improvement had a low certainty (mean difference = −5.21, 95% CI [−7.15, −3.26]) due to high heterogeneity. Substantial improvements in pain and disability were observed when pelvic floor muscle training was added to standard physical therapy, with low certainty of evidence supporting these findings. Whereas pelvic floor muscle training substantially improved pain compared to other interventions, there was no marked improvement in disability. Conclusion: Pelvic floor muscle training is potentially beneficial in addition to physical therapy for reducing low back pain, particularly in pregnancy-related cases. However, the evidence should be interpreted considering the quality and risk of bias. [ABSTRACT FROM AUTHOR]

Background: A comprehensive understanding of the impact of the COVID-19 pandemic on childhood nutrition is crucial for devising effective mitigation strategies. However, existing knowledge regarding the pandemic's effect on childhood nutritional status remains limited. Furthermore, research focusing on young children aged 0–3 years is scarce. Methods: Leveraging the outbreak that originated in Wuhan in Dec 2019, the epicenter of China's first and largest outbreak, and national survey and statistical yearbook data, this study conducted a natural experimental analysis with the consideration of geographical exposure, temporal exposure and survey cohort effects to investigate the pandemic's impacts on varying nutritional indicators of infants and toddlers aged 0–36 months. A comprehensive set of sensitivity analyses, robustness checks and falsification tests were conducted. The potential heterogeneities across socioeconomic and age groups were also examined. Results: The pandemic was robustly predictive of a higher weight-for-age z-score (WAZ) and length/height-for-age z-score (HAZ), and a lower likelihood of underweight. The effects of the pandemic on increasing WAZ and reducing underweight were more pronounced among children from economically disadvantaged backgrounds or aged 0–12 months. Additionally, the increasing HAZ was primarily among children from households with lower family income. Moreover, the pandemic was negatively linked to the body mass index (BMI)-for-age z-score (BAZ) of children residing in more developed cities, and positively linked to overweight/obesity among children aged 13–24 months. Conclusions: This study adds to a more comprehensive understanding of the impact of the COVID-19 pandemic on childhood nutrition. Notably, the findings highlight that weight gain attributable to the pandemic was predominantly among vulnerable children from disadvantaged backgrounds and younger age groups, who were already at a higher risk of overweight/obesity before the pandemic. Consequently, our findings imply the necessity of greater caution to the widened gap in childhood malnutrition post-pandemic. Furthermore, the study emphasizes the importance of implementing adaptable strategies with the consideration of social justice to safeguard all children's right to optimal growth from exogenous shocks and to achieve the children-related SDGs by 2030. [ABSTRACT FROM AUTHOR]

Mental health conditions such as anxiety, depression, aggression, and inattention are common in autistic youth and are challenging to treat by community providers. We aim to parse the heterogeneity of autism based on dimensions of executive function and determine whether specific executive function profiles are differentially related to psychiatric symptoms. Parents of 397 well-characterized 8–14-year-old autistic children without an intellectual disability reported on their child's executive function skills and psychiatric symptoms. We applied a data-driven, graph theory-based, community-detection approach to a common executive function measure, revealing three distinct executive function profile subgroups. Despite having similar social challenges, the executive function subgroups differed on anxiety, aggression, affect, and inattention symptoms. Results support the need for more intensive subtyping with autistic youth to develop appropriate, individualized mental health treatments and supports. Characterizing youth through neurocognitive strengths and challenges can guide the development of precision medicine, allowing for more meaningful, specialized treatment. Many autistic people struggle with mental health problems like anxiety, depression, inattention, and aggression, which can be challenging to treat. Executive function challenges, which impact many autistic individuals, may serve as a risk factor for mental health problems or make treating mental health conditions more difficult. While some people respond well to medication or therapy, others do not. This study tried to understand if there are different subgroups of autistic young people who may have similar patterns of executive function strengths and challenges—like flexibility, planning, self-monitoring, and emotion regulation. Then, we investigated whether executive function subgroups were related to mental health problems in autistic youth. We found three different types of executive function subgroups in autistic youth, each with different patterns of mental health problems. This helps us identify specific profiles of executive function strengths and challenges that may be helpful with identifying personalized supports, services, and treatment strategies for mental health conditions. [ABSTRACT FROM AUTHOR]

Objectives: Numerous studies have investigated the predictive significance of the platelet-to-lymphocyte ratio (PLR) in oral squamous cell carcinoma(OSCC). However, the results of studies on its prognostic value remain controversial. This study aims to evaluate the prognostic significance of the PLR in patients with OSCC. Materials and methods: We conducted a comprehensive search of PubMed, Embase, the Cochrane Library, and Web of Science databases for all studies investigating the association between PLR and prognosis in OSCC, from the inception of each database up to November 2023. The outcome measures included overall survival (OS), disease-free survival (DFS) and disease-specific survival (DSS). To identify potential sources of heterogeneity, sensitivity and subgroup analyses were performed, alongside an assessment of publication bias. The analyses in this study were conducted using RevMan and Stata software. Results: According to the inclusion criteria, 20 articles were included, including 5,714 patients.The meta-analysis revealed that an elevated PLR adversely affects OS (HR = 1.58; 95% CI: 1.29–1.94; p < 0.0001), DFS (HR = 1.71; 95% CI: 1.20–2.42; p < 0.003), and DSS (HR = 2.41; 95% CI: 1.79–3.25; p < 0.00001). The results of the subgroup analysis showed that a preoperative high PLR was associated with reduced OS when the PLR threshold was ≤ 150 (HR = 1.49, P = 0.0003).When the PLR threshold was > 150, Preoperative PLR was not significantly associated with the prediction of overall survival (OS) in OSCC. (P > 0.05). Conclusion: The prognostic significance of PLR in patients with oral cancer is evident in terms of OS, DSS, and DFS. Nonetheless, it is crucial to note that the predictive value of PLR might depend on specific threshold values. [ABSTRACT FROM AUTHOR]

To improve the validity of self-report measures, researchers should control for response style (RS) effects, which can be achieved with IRTree models. A traditional IRTree model considers a response as a combination of distinct decision-making processes, where the substantive trait affects the decision on response direction, while decisions about choosing the middle category or extreme categories are largely determined by midpoint RS (MRS) and extreme RS (ERS). One limitation of traditional IRTree models is the assumption that all respondents utilize the same set of RS in their response strategies, whereas it can be assumed that the nature and the strength of RS effects can differ between individuals. To address this limitation, we propose a mixture multidimensional IRTree (MM-IRTree) model that detects heterogeneity in response strategies. The MM-IRTree model comprises four latent classes of respondents, each associated with a different set of RS traits in addition to the substantive trait. More specifically, the class-specific response strategies involve (1) only ERS in the "ERS only" class, (2) only MRS in the "MRS only" class, (3) both ERS and MRS in the "2RS" class, and (4) neither ERS nor MRS in the "0RS" class. In a simulation study, we showed that the MM-IRTree model performed well in recovering model parameters and class memberships, whereas the traditional IRTree approach showed poor performance if the population includes a mixture of response strategies. In an application to empirical data, the MM-IRTree model revealed distinct classes with noticeable class sizes, suggesting that respondents indeed utilize different response strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: Immune checkpoint inhibitors (ICIs) are a common and growing type of cancer treatment, but they can cause side effects. This study aims to understand these side effects in patients with kidney cancer and bladder cancer. We compared the types and severity of side effects between these two groups to determine if there are differences. We also compared them to other tumor types to see if there is a unique pattern. The findings will help doctors better predict and manage side effects. This will hopefully lead to improving patient care for those receiving ICI treatment. Background: Immune checkpoint inhibitors (ICIs) have become common lines of therapy for genitourinary cancers (GUcs). Given their widespread use, understanding the risk factors, comparative profiles, and timing of immune-related adverse events (irAEs) is essential. Methods: We created an IRB-approved retrospective registry of all patients who received at least one dose of an ICI for any indication between 1 February 2011 and 7 April 2022 at a comprehensive cancer center and its outreach clinics. Dichotomous outcomes were modeled using multivariable logistic regression. Survival outcomes were compared using multivariable Cox regression. Results: Among 3101 patients, 196 had renal cell carcinoma (RCC) and 170 had urothelial tumors. RCC patients were more likely to experience irAEs (OR 1.78; 95% CI 1.32–2.39), whereas urothelial carcinoma patients were not (OR 1.22; 95% CI 0.88–1.67). RCC patients were more prone to dermatitis, thyroiditis, acute kidney injury, and myocarditis, compared to other tumors, while urothelial carcinoma patients were not. The impact of irAEs on survival was not significantly different for GUcs compared to other tumors. Conclusions: RCC primaries have a significantly different irAE profile than most tumors, as opposed to urothelial primaries. Further, RCC was more likely to experience any irAEs. Heterogeneity of survival benefits by irAEs was not seen. [ABSTRACT FROM AUTHOR]

Background: Whether intensive glucose control reduces mortality in critically ill patients remains uncertain. Patient-level meta-analyses can provide more precise estimates of treatment effects than are currently available. Methods: We pooled individual patient data from randomized trials investigating intensive glucose control in critically ill adults. The primary outcome was in-hospital mortality. Secondary outcomes included survival to 90 days and time to live cessation of treatment with vasopressors or inotropes, mechanical ventilation, and newly commenced renal replacement. Severe hypoglycemia was a safety outcome. Results: Of 38 eligible trials (n=29,537 participants), 20 (n=14,171 participants) provided individual patient data including in-hospital mortality status for 7059 and 7049 participants allocated to intensive and conventional glucose control, respectively. Of these 1930 (27.3%) and 1891 (26.8%) individuals assigned to intensive and conventional control, respectively, died (risk ratio, 1.02; 95% confidence interval [CI], 0.96 to 1.07; P=0.52; moderate certainty). There was no apparent heterogeneity of treatment effect on in-hospital mortality in any examined subgroups. Intensive glucose control increased the risk of severe hypoglycemia (risk ratio, 3.38; 95% CI, 2.99 to 3.83; P<0.0001). Conclusions: Intensive glucose control was not associated with reduced mortality risk but increased the risk of severe hypoglycemia. We did not identify a subgroup of patients in whom intensive glucose control was beneficial. (Funded by the Australian National Health and Medical Research Council and others; PROSPERO number CRD42021278869.) [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigates the role of visual intratumor heterogeneity (ITH) in breast cancer progression. By analyzing histologic images from the Carolina Breast Cancer Study (CBCS) and the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data using advanced image processing and machine learning techniques, we developed a measure of tumor heterogeneity based on visual features. Our findings indicate that tumors with low visual heterogeneity exhibited a higher risk of recurrence and were more likely to come from patients whose tumors comprised of only one subclone or had a TP53 mutation. Conversely, high visual heterogeneity was correlated with a more favorable prognosis. These results suggest that visual heterogeneity provides complementary information to molecular markers. A comprehensive understanding of both the visual and molecular aspects of heterogeneity has the potential to offer novel insights for treatment strategies. High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or 'visual' heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity. We used VGG16 to train an image classifier to identify unique, patient-specific visual features in 1655 breast tumors (5907 core images) from the Carolina Breast Cancer Study (CBCS). Extracted features for images, as well as the epithelial and stromal image components, were hierarchically clustered, and visual heterogeneity was defined as a greater distance between images from the same patient. We assessed the association between visual heterogeneity, clinical features, and DNA-based molecular heterogeneity using generalized linear models, and we used Cox models to estimate the association between visual heterogeneity and tumor recurrence. Basal-like and ER-negative tumors were more likely to have low visual heterogeneity, as were the tumors from younger and Black women. Less heterogeneous tumors had a higher risk of recurrence (hazard ratio = 1.62, 95% confidence interval = 1.22–2.16), and were more likely to come from patients whose tumors were comprised of only one subclone or had a TP53 mutation. Associations were similar regardless of whether the image was based on stroma, epithelium, or both. Histologic heterogeneity adds complementary information to commonly used molecular indicators, with low heterogeneity predicting worse outcomes. Future work integrating multiple sources of heterogeneity may provide a more comprehensive understanding of tumor progression. [ABSTRACT FROM AUTHOR]

Distributed network studies and multisite studies assess drug safety and effectiveness in diverse populations by pooling information. Targeting groups of clinical or policy interest (including specific sites or site combinations) and applying weights based on effect measure modifiers (EMMs) prior to pooling estimates within multisite studies may increase interpretability and improve precision. We simulated a 4-site study, standardized each site using inverse odds weights (IOWs) to resemble the 3 smallest sites or the smallest site, estimated IOW-weighted risk differences (RDs), and combined estimates with inverse variance weights (IVWs). We also created an artificial distributed network in the Clinical Practice Research Datalink (CPRD) Aurum consisting of 1 site for each geographic region. We compared metformin and sulfonylurea initiators with respect to mortality, targeting the smallest region. In the simulation, IOWs reduced differences between estimates and increased precision when targeting the 3 smallest sites or the smallest site. In the CPRD Aurum study, the IOW + IVW estimate was also more precise (smallest region: RD = 5.41% [95% CI, 1.03-9.79]; IOW + IVW estimate: RD = 3.25% [95% CI, 3.07-3.43]). When performing pharmacoepidemiologic research in distributed networks or multisite studies in the presence of EMMs, designation of target populations has the potential to improve estimate precision and interpretability. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

Background According to the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) criteria, both immunohistochemical HER2 (3+) and HER2 (2+)/in situ hybridization (ISH) amplified [HER2 (2+)/ISH+] breast cancers (BCs) fall under the HER2-positive BC category. However, there is a lack of studies exploring the difference of neoadjuvant therapeutic response between patients with HER2 (3+) and HER2 (2+)/ISH+ early BC. We aimed to evaluate the neoadjuvant therapeutic response, long-term outcome, and intrinsic subtype heterogeneity between HER2 (3+) and HER2 (2+)/ISH+ BC. Methods We examined 2 distinct cohorts. Cohort 1 (C1) encompassed 2648 patients with HER2-positive early BC diagnoses, and they received neoadjuvant therapy (NT) and surgery between January 1, 2009 and December 31, 2022, from the Shanghai Jiao Tong University Breast Cancer Data Base. Cohort 2 (C2) comprised 135 patients with early-stage HER2-positive BC who underwent NT and surgery at Henan Cancer Hospital from January 1, 2021, to December 31, 2022. These patients had available genomic and transcriptomic data at their disposal. C1 and C2 were further categorized into 2 patient cohorts as follows: (1) patients with IHC HER2 (3+) early BC [HER2 (3+) group], (2) patients with HER2 (2+)/ISH+ early BC [HER2 (2+)/ISH+ group]. Among those excluded from the analysis were patients < 18 years or >80 years of age. Clinicopathological parameters, long-term outcomes, and intrinsic subtypes were analyzed. Results In the C1 population, 83.7% had HER2 (3+) BC, while 16.3% had HER2 (2+)/ISH+ BC. Patients with HER2 (3+) had a significantly higher pathological complete response (PCR) rate (38.9%) than patients with HER2 (2+)/ISH+ (18.1%; P  < .001), but the disease-free survival (DFS) was comparable after a median follow-up of 29 months (P  = .556). The addition of trastuzumab or trastuzumab plus pertuzumab to neoadjuvant chemotherapy (NAC) improved PCR rates and DFS in HER2 (3+) BC but not in HER2 (2+)/ISH+ BC. In the C2 population, 97.75% HER2 (3+) and 52.17% HER2 (2+)/ISH+ were HER2 enriched (HER2E) subtype (P  < .001). HER2E showed increased PCR rates compared to non-HER2E (P  = .004). Conclusions Compared to HER2 (3+) BC, the limited effectiveness of neoadjuvant trastuzumab and pertuzumab therapy for HER2 (2+)/ISH+ BC is due to subtype heterogeneity. Reassessment of targeted therapy efficacy in patients with HER2 (2+)/ISH+ BC is essential. [ABSTRACT FROM AUTHOR]

Simple Summary: Medulloblastoma (MB) is the most common malignant brain tumor in children and has a dismal prognosis. A challenge with MB is identifying patients who could be candidates for reduced doses of radiation therapy, but are still treated effectively, as well as those that need intensified doses. Recently, MB was classified into four molecular subgroups with distinct clinical outcomes (WNT, SHH, Group 3, and Group 4). Though two of these subgroups (SHH and Group 4) are known for their intermediate prognosis, wide disparities of outcomes have been reported within each of these subgroups. This work aims to develop a prognostic signature using radiomics (computationally derived tumor measurements), acquired on MRI scans, to risk-stratify patients within the SHH and Group 4 subgroups. Our signature includes two key attributes that capture aspects of the disease microenvironment. We believe that our signature will provide a better understanding of the disease's heterogeneity and, hence, develop better personalized treatment plans. Medulloblastoma (MB) is the most frequent malignant brain tumor in children with extensive heterogeneity that results in varied clinical outcomes. Recently, MB was categorized into four molecular subgroups, WNT, SHH, Group 3, and Group 4. While SHH and Group 4 are known for their intermediate prognosis, studies have reported wide disparities in patient outcomes within these subgroups. This study aims to create a radiomic prognostic signature, medulloblastoma radiomics risk (mRRisk), to identify the risk levels within the SHH and Group 4 subgroups, individually, for reliable risk stratification. Our hypothesis is that this signature can comprehensively capture tumor characteristics that enable the accurate identification of the risk level. In total, 70 MB studies (48 Group 4, and 22 SHH) were retrospectively curated from three institutions. For each subgroup, 232 hand-crafted features that capture the entropy, surface changes, and contour characteristics of the tumor were extracted. Features were concatenated and fed into regression models for risk stratification. Contrasted with Chang stratification that did not yield any significant differences within subgroups, significant differences were observed between two risk groups in Group 4 (p = 0.04, Concordance Index (CI) = 0.82) on the cystic core and non-enhancing tumor, and SHH (p = 0.03, CI = 0.74) on the enhancing tumor. Our results indicate that radiomics may serve as a prognostic tool for refining MB risk stratification, towards improved patient care. [ABSTRACT FROM AUTHOR]

Purpose Multidrug-resistant (MDR) infections are challenging to treat due to underlying patient conditions, pathogen characteristics, and high antibiotic resistance rates. As newer antibiotic therapies come to market, limited data exist about their real-world utilization. Methods This was a national retrospective cohort study of ceftazidime/avibactam (approved in 2015) utilization among inpatients from the Veterans Affairs (VA) Healthcare System, from 2015 through 2021. Joinpoint regression was used to estimate time trends in utilization. Results Ceftazidime/avibactam use increased by 52.3% each year (days of therapy per 1,000 bed days; 95% confidence interval, 12.4%-106.4%). We identified 1,048 unique predominantly male (98.3%) and white (66.2%; Black, 27.7%) patients treated with ceftazidime/avibactam, with a mean (SD) age of 71.5 (11.9) years. The most commonly isolated organisms were Pseudomonas aeruginosa (36.3%; carbapenem resistant, 80.6%; MDR, 65.0%) and Klebsiella species (34.1%; carbapenem resistant, 78.4%; extended-spectrum cephalosporin resistant, 90.7%). Common comorbid conditions included hypertension (74.8%), nervous system disorders (60.2%), diabetes mellitus (48.7%), and cancer (45.1%). Median time to ceftazidime/avibactam initiation from admission was 6 days, with a median of 3 changes in therapy before ceftazidime/avibactam initiation and a subsequent median length of inpatient stay of 14 days (median of 8 days of ceftazidime/avibactam therapy). Treatment heterogeneity was high, both before ceftazidime/avibactam initiation (89.6%) and during ceftazidime/avibactam treatment (85.6%), and common concomitant antibiotics included vancomycin (41.4%), meropenem (24.1%), cefepime (15.2%), and piperacillin/tazobactam (15.2%). The inpatient mortality rate was 23.6%, and 20.8% of patients had a subsequent admission with ceftazidime/avibactam treatment. Conclusion Utilization of ceftazidime/avibactam increased from 2015 to 2021 in the national VA Healthcare System. Ceftazidime/avibactam was utilized in complex, difficult-to-treat patients, with substantial treatment heterogeneity and variation in the causative organism and culture sites. [ABSTRACT FROM AUTHOR]

Methylphenidate (MPH), a first-line treatment for attention-deficit hyperactivity disorder (ADHD) management, has been the focus of debate for decades regarding its effect on growth. The aim of this PRISMA meta-analysis was to determine the effect of MPH on height in children/adolescents with ADHD and its predictive factors based on literature reports. Available full-text articles were systematically reviewed to identify clinical studies of pediatric ADHD patients with height Z-score (HZS) data for monotherapy MPH-treated and non-treated groups. We estimated standardized mean differences (SMDs) of HZS or its changes from baseline (ΔHZS) between groups, then identified associated factors through subgroup analyses and meta-regression. For before–after treatment studies, the paired standard errors of ΔHZS were re-estimated to demonstrate in the forest plot. Risk of bias was analyzed using the Newcastle–Ottawa Scale. Among the 29 eligible studies, 26 reported ΔHZS with self-control groups, and ΔHZS or absolute HZS were compared to other external controls in 11 studies. A significant reduction was observed between post-MHP and pre-MPH use, with high heterogeneity (SMD = – 0.40; 95% confidence interval = [ – 0.54, – 0.27]; I2 = 91%). The study region, ADHD subtype, and stimulant-naïve status of patients at baseline may modify the effect on HZS. Because of the high clinical heterogeneity in observational studies, clinicians should consider the negative effect of MPH on height in ADHD patients by determining whether patients fulfill appropriate high-risk criteria. Further well-designed longitudinal studies are required to better quantify this effect, especially with prolonged treatment. [ABSTRACT FROM AUTHOR]

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7):1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research. [ABSTRACT FROM AUTHOR]

The purpose of this study is to illustrate how matching approaches based on different balancing scores lead to variations in the treatment effect estimators. We introduced a case study evaluating the effect of anti-thrombotic agents on the severe progression of patients with intracerebral hemorrhage. We extracted subpopulations based on propensity and prognostic scores and then estimated the relative risk between the treatment groups. Furthermore, to illustrate the situation where the treatment effect estimates varied depending on employed balancing scores, we conducted a simulation experiment. In the case study, the matching using different balancing scores extracted subpopulations with different characteristics. Then, the estimated relative risk (95% confidence interval) was 1.27 (0.98–1.94) among the propensity score matched cohort, whereas it was 0.91 (0.76–1.08) among the prognostic score matched cohort. In the simulation experiments, the results indicated that the matching schemes based on different balancing scores created distinct matched cohorts, leading to varying estimates under treatment effect heterogeneity. Moreover, the variability of the estimated effect becomes substantial when there are covariates strongly related to the dependent variable of the nuisance functions. The difference in the selected subpopulation via matching based on different balancing scores is a thoughtful factor that can result in different estimates when there is effect heterogeneity. In practice, we recommend assessing the characteristics of the matched subpopulation and employing the balancing score that can estimate the treatment effect among the target population of interest in each study. Plain Language Summary: This study aimed to understand why different methods of matching patients in a study can lead to different estimates of how well a treatment works. We used a real example of a study where the treatment's effectiveness varied for different patients and compared how different methods of matching patients affected the results. We also did a series of experiments to see how the different methods affected estimates of treatment effect when the effectiveness varied among patients. Our study found that the different matching methods extracted the different patients. The difference in the matched patients led to very different results, especially when the treatment's effectiveness was not the same for all patients. Therefore, it is important to check the characteristics of patients after matching and consider whether the treatment's effectiveness is not the same for all patients. [ABSTRACT FROM AUTHOR]

Background: Chronic pain is one of the most common drivers of healthcare utilization and a marked domain for health disparities, as African American/Black populations experience high rates of chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence-based integrative medicine, and medical group visits. In a parent randomized controlled trial, this approach was tested as an adjunct treatment in a diverse, medically underserved population with chronic pain and depression. Objective: To determine race-based heterogeneity in the effects of a mindfulness based treatment for chronic pain. Methods: This secondary analysis of the parent trial assessed heterogeneity of treatment effects along racialized identity in terms of primary patient-reported pain outcomes in a racially diverse sample suffering from chronic pain and depression. The analytic approach examined comorbidities and sociodemographics between racialized groups. RMANOVAs examined trajectories in pain outcomes (average pain, pain severity, and pain interference) over three timepoints (baseline, 9, and 21 weeks) between participants identifying as African American/Black (n = 90) vs White (n = 29) across both intervention and control conditions. Results: At baseline, African American/Black participants had higher pain severity and had significantly different age, work status, and comorbidity profiles. RMANOVA models also identified significant race-based differences in the response to the parent IMGV intervention. There was reduced pain severity in African American/Black subjects in the IMGV condition from baseline to 9 weeks. This change was not observed in White participants over this time period. However, there was a reduction in pain severity in White participants over the subsequent interval from 9 to 21 week where IMGV had no significant effect in African American/Black subjects during this latter time period. Conclusion: Interactions between pain and racialization require further investigation to understand how race-based heterogeneity in the response to integrative medicine treatments for chronic pain contribute to the broader landscape of health inequity. [ABSTRACT FROM AUTHOR]

Summary: Importance: The effectiveness of anti‐obesity medications for children and adolescents is unclear. Objective: To update the evidence on the benefits and harms of anti‐obesity medication. Data Sources: Cochrane CENTRAL, MEDLINE, ClinicalTrials.gov and WHO ICTRP (1/1/16–17/3/23). Study Selection: Randomized controlled trials ≥6 months in people <19 years living with obesity. Data Extraction and Synthesis: Screening, data extraction and quality assessment conducted in duplicate, independently. Main Outcomes and Measures: Body mass index (BMI): 95th percentile BMI, adverse events and quality of life. Results: Thirty‐five trials (N = 4331), follow‐up: 6–24 months; age: 8.8–16.3 years; BMI: 26.2–41.7 kg/m2. Moderate certainty evidence demonstrated a −1.71 (95% confidence interval [CI]: −2.27 to −1.14)‐unit BMI reduction, ranging from −0.8 to −5.9 units between individual drugs with semaglutide producing the largest reduction of −5.88 kg/m2 (95% CI: −6.99 to −4.77, N = 201). Drug type explained ~44% of heterogeneity. Low certainty evidence demonstrated reduction in 95th percentile BMI: −11.88 percentage points (95% CI: −18.43 to −5.30, N = 668). Serious adverse events and study discontinuation due to adverse events did not differ between medications and comparators, but medication dose adjustments were higher compared to comparator (10.6% vs 1.7%; RR = 3.74 [95% CI: 1.51 to 9.26], I2 = 15%), regardless of approval status. There was a trend towards improved quality of life. Evidence gaps exist for children, psychosocial outcomes, comorbidities and weight loss maintenance. Conclusions and Relevance: Anti‐obesity medications in addition to behaviour change improve BMI but may require dose adjustment, with 1 in 100 adolescents experiencing a serious adverse event. [ABSTRACT FROM AUTHOR]

Background: Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention. Methods: We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary twosample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings. Results: After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data. Conclusion: By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD. [ABSTRACT FROM AUTHOR]

Simple Summary: This clinical study explored the use of the positron emission tomography radiotracer, [18F]FSPG, for cancer imaging. [18F]FSPG measures a process involved in antioxidant production, which is important for cancer prognosis. We compared the distribution of this imaging agent in subjects with head and neck squamous cell cancer (HNSCC) and non-small-cell lung cancer (NSCLC). Results showed similar distribution in healthy organs but varied uptake in tumors, both between subjects and across different lesions. Although [18F]FSPG PET/CT offers insights into individual tumor behavior, its diagnostic potential is limited due to this variability in tumor uptake. Variability in [18F]FSPG retention, however, may provide crucial information about how tumors respond to therapy and mechanisms of treatment resistance. Background: (4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid ([18F]FSPG) positron emission tomography/computed tomography (PET/CT) provides a readout of system xc− transport activity and has been used for cancer detection in clinical studies of different cancer types. As system xc− provides the rate-limiting precursor for glutathione biosynthesis, an abundant antioxidant, [18F]FSPG imaging may additionally provide important prognostic information. Here, we performed an analysis of [18F]FSPG radiotracer distribution between primary tumors, metastases, and normal organs from cancer patients. We further assessed the heterogeneity of [18F]FSPG retention between cancer types, and between and within individuals. Methods: This retrospective analysis of prospectively collected data compared [18F]FSPG PET/CT in subjects with head and neck squamous cell cancer (HNSCC, n = 5) and non-small-cell lung cancer (NSCLC, n = 10), scanned at different institutions. Using semi-automated regions of interest drawn around tumors and metastases, the maximum standardized uptake value (SUVmax), SUVmean, SUV standard deviation and SUVpeak were measured. [18F]FSPG time–activity curves (TACs) for normal organs, primary tumors and metastases were subsequently compared to 18F-2-fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT at 60 min post injection (p.i.). Results: The mean administered activity of [18F]FSPG was 309.3 ± 9.1 MBq in subjects with NSCLC and 285.1 ± 11.3 MBq in those with HNSCC. The biodistribution of [18F]FSPG in both cohorts showed similar TACs in healthy organs from cancer patients. There was no statistically significant overall difference in the average SUVmax of tumor lesions at 60 min p.i. for NSCLC (8.1 ± 7.1) compared to HNSCC (6.0 ± 4.1; p = 0.29) for [18F]FSPG. However, there was heterogeneous retention between and within cancer types; the SUVmax at 60 min p.i. ranged from 1.4 to 23.7 in NSCLC and 3.1–12.1 in HNSCC. Conclusion: [18F]FSPG PET/CT imaging from both NSCLC and HNSCC cohorts showed the same normal-tissue biodistribution, but marked tumor heterogeneity across subjects and between lesions. Despite rapid elimination through the urinary tract and low normal-background tissue retention, the diagnostic potential of [18F]FSPG was limited by variability in tumor retention. As [18F]FSPG retention is mediated by the tumor's antioxidant capacity and response to oxidative stress, this heterogeneity may provide important insights into an individual tumor's response or resistance to therapy. [ABSTRACT FROM AUTHOR]

This article clarifies how the biostatistical literature on time-varying treatments (TVT) can provide tools for dealing with time-varying confounding in difference-in-differences (DiD) studies. I use a simulation study to compare the bias and standard error of inverse probability weighting estimators from the TVT framework, a DiD framework, and hybrid approaches that combine ideas from both frameworks. I simulated longitudinal data with treatment effect heterogeneity over multiple time points using linear and logistic models. Simulation settings looked at both time-invariant confounders and time-varying confounders affected by prior treatment. Estimators that combined ideas from both frameworks had lower bias than standard TVT and DiD estimators when assumptions were unmet. The TVT framework provides estimation tools that can complement DiD tools in a wide range of applied settings. It also provides alternate estimands for consideration in policy settings. [ABSTRACT FROM AUTHOR]

Methods for extending—generalizing or transporting—inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and nonrandomized groups exchangeable. Yet, decision makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model--based (g-formula), weighting, and augmented weighting estimators. We consider estimating subgroup-specific average treatment effects in the target population and its nonrandomized subset, and we provide methods that are appropriate both for nested and non-nested trial designs. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (North America, 1975–1996) to compare the effect of surgery plus medical therapy versus medical therapy alone for chronic coronary artery disease in subgroups defined by history of myocardial infarction. [ABSTRACT FROM AUTHOR]

Due to the many benefits of understanding treatment effect heterogeneity in a clinical trial, an exploratory post hoc subgroup analysis is often performed to find subpopulations of patients with conditional average treatment effect that suggests better treatment efficacy than in the overall population. A naive re-substitution approach uses all available data to identify a subgroup and then proceeds with estimation and inference using the same data set. This approach generally leads to an overly optimistic estimate of conditional average treatment effect. In this article, in a post hoc analysis, we estimate the target optimal subgroup through maximizing a utility function, from candidates systematically identified with a penalized regression. We then compare two resampling-based bias-correction methods, cross-validation and debiasing bootstrap, for obtaining approximately unbiased estimates and valid inference of conditional average treatment effect in the identified subgroup, with either an empirical or an augmented estimator. Our results show that both the cross-validation and the debiasing bootstrap methods reduce the re-substitution bias effectively. The cross-validation method appears to have less biased point estimates, smaller standard error estimates, but poorer coverages than the debiasing bootstrap method when using the empirical estimator and the sample size is moderate. Using the augmented estimator in the debiasing bootstrap method leads to less biased point estimates but poorer coverages. We conclude that bias correction should be a part of every exploratory post hoc subgroup analysis to eliminate re-substitution bias and to obtain a proper confidence interval for the estimated conditional average treatment effect in the selected subgroup. [ABSTRACT FROM AUTHOR]

A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered. [ABSTRACT FROM AUTHOR]

Generalizability methods are increasingly used to make inferences about the effect of interventions in target populations using a study sample. Most existing methods to generalize effects from sample to population rely on the assumption that subgroup-specific effects generalize directly. However, researchers may be concerned that in fact subgroup-specific effects differ between sample and population. In this brief report, we explore the generalizability of subgroup effects. First, we derive the bias in the sample average treatment effect estimator as an estimate of the population average treatment effect when subgroup effects in the sample do not directly generalize. Next, we present a Monte Carlo simulation to explore bias due to unmeasured heterogeneity of subgroup effects across sample and population. Finally, we examine the potential for bias in an illustrative data example. Understanding the generalizability of subgroup effects may lead to increased use of these methods for making externally valid inferences of treatment effects using a study sample. [ABSTRACT FROM AUTHOR]

We consider the problem of designing a confirmatory randomized trial for comparing two treatments versus a common control in two disjoint subpopulations. The subpopulations could be defined in terms of a biomarker or disease severity measured at baseline. The goal is to determine which treatments benefit which subpopulations. We develop a new class of adaptive enrichment designs tailored to solving this problem. Adaptive enrichment designs involve a preplanned rule for modifying enrollment based on accruing data in an ongoing trial. At the interim analysis after each stage, for each subpopulation, the preplanned rule may decide to stop enrollment or to stop randomizing participants to one or more study arms. The motivation for this adaptive feature is that interim data may indicate that a subpopulation, such as those with lower disease severity at baseline, is unlikely to benefit from a particular treatment while uncertainty remains for the other treatment and/or subpopulation. We optimize these adaptive designs to have the minimum expected sample size under power and Type I error constraints. We compare the performance of the optimized adaptive design versus an optimized nonadaptive (single stage) design. Our approach is demonstrated in simulation studies that mimic features of a completed trial of a medical device for treating heart failure. The optimized adaptive design has $25\%$ smaller expected sample size compared to the optimized nonadaptive design; however, the cost is that the optimized adaptive design has $8\%$ greater maximum sample size. Open-source software that implements the trial design optimization is provided, allowing users to investigate the tradeoffs in using the proposed adaptive versus standard designs. [ABSTRACT FROM AUTHOR]

Background: Retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the desire to learn all we can from exhaustive data measurements on trial participants motivates the inclusion of such analyses within RCTs. Moreover, widespread advances in machine learning (ML) methods hold potential to utilise such data to identify subjects exhibiting heterogeneous treatment response.Methods: We present a novel analysis strategy for detecting TEH in randomised data using ML methods, whilst ensuring proper control of the false positive discovery rate. Our approach uses random data partitioning with statistical or ML-based prediction on held-out data. This method can test for both crossover TEH (switch in optimal treatment) and non-crossover TEH (systematic variation in benefit across patients). The former is done via a two-sample hypothesis test measuring overall predictive performance. The latter is done via 'stacking' the ML predictors alongside a classical statistical model to formally test the added benefit of the ML algorithm. An adaptation of recent statistical theory allows for the construction of a valid aggregate p value. This testing strategy is independent of the choice of ML method.Results: We demonstrate our approach with a re-analysis of the SEAQUAMAT trial, which compared quinine to artesunate for the treatment of severe malaria in Asian adults. We find no evidence for any subgroup who would benefit from a change in treatment from the current standard of care, artesunate, but strong evidence for significant TEH within the artesunate treatment group. In particular, we find that artesunate provides a differential benefit to patients with high numbers of circulating ring stage parasites.Conclusions: ML analysis plans using computational notebooks (documents linked to a programming language that capture the model parameter settings, data processing choices, and evaluation criteria) along with version control can improve the robustness and transparency of RCT exploratory analyses. A data-partitioning algorithm allows researchers to apply the latest ML techniques safe in the knowledge that any declared associations are statistically significant at a user-defined level. [ABSTRACT FROM AUTHOR]

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs. [ABSTRACT FROM AUTHOR]

Stepped-wedge cluster randomized trials, which randomize clusters of subjects to treatment sequences in which clusters switch from control to intervention conditions, are being conducted with increasing frequency. Due to the real-world nature of this design, methodological and implementation challenges are ubiquitous. To account for such challenges, more complex statistical models to plan studies and analyze data are required. In this paper, we consider stepped-wedge trials that accommodate treatment effect heterogeneity across clusters, implementation periods during which no data are collected, or both treatment effect heterogeneity and implementation periods. Previous work has shown that the sequence-period cells of a stepped-wedge design contribute unequal amounts of information to the estimation of the treatment effect. In this paper, we extend that work by considering the amount of information available for the estimation of the treatment effect in each sequence-period cell, sequence, and period of stepped-wedge trials with more complex designs and outcome models. When either treatment effect heterogeneity and/or implementation periods are present, the pattern of information content of sequence-period cells tends to be clustered around the times of the switch from control to intervention condition, similarly to when these complexities are absent. However, the presence and degree of treatment effect heterogeneity and the number of implementation periods can influence the information content of periods and sequences markedly. [ABSTRACT FROM AUTHOR]

The literature on causal effect estimation tends to focus on the population mean estimand, which is less informative as medical treatments are becoming more personalized and there is increasing awareness that subpopulations of individuals may experience a group-specific effect that differs from the population average. In fact, it is possible that there is underlying systematic effect heterogeneity that is obscured by focusing on the population mean estimand. In this context, understanding which covariates contribute to this treatment effect heterogeneity (TEH) and how these covariates determine the differential treatment effect (TE) is an important consideration. Towards such an understanding, this paper briefly reviews three approaches used in making causal inferences and conducts a simulation study to compare these approaches according to their performance in an exploratory evaluation of TEH when the heterogeneous subgroups are not known a priori. Performance metrics include the detection of any heterogeneity, the identification and characterization of heterogeneous subgroups, and unconfounded estimation of the TE within subgroups. The methods are then deployed in a comparative effectiveness evaluation of drug-eluting versus bare-metal stents among 54 099 Medicare beneficiaries in the continental United States admitted to a hospital with acute myocardial infarction in 2008. [ABSTRACT FROM AUTHOR]

Cluster randomized trials are frequently used in health service evaluation. It is common practice to use an analysis model with a random effect to allow for clustering at the analysis stage. In designs where clusters are exposed to both control and treatment conditions, it may be of interest to examine treatment effect heterogeneity across clusters. In designs where clusters are not exposed to both control and treatment conditions, it can also be of interest to allow heterogeneity in the degree of clustering between arms. These two types of heterogeneity are related. It has been proposed in both parallel cluster trials, stepped-wedge, and other cross-over designs that this heterogeneity can be allowed for by incorporating additional random effect(s) into the model. Here, we show that the choice of model parameterization needs careful consideration as some parameterizations for additional heterogeneity induce unnecessary or implausible assumptions. We suggest more appropriate parameterizations, discuss their relative advantages, and demonstrate the implications of these model choices using a real example of a parallel cluster trial and a simulated stepped-wedge trial. [ABSTRACT FROM AUTHOR]

Aims To compare randomized controlled trial (RCT) sample treatment effects with the population effects of substance use disorder (SUD) treatment. Design Statistical weighting was used to re-compute the effects from 10 RCTs such that the participants in the trials had characteristics that resembled those of patients in the target populations. Settings Multi-site RCTs and usual SUD treatment settings in the United States. Participants A total of 3592 patients in 10 RCTs and 1 602 226 patients from usual SUD treatment settings between 2001 and 2009. Measurements Three outcomes of SUD treatment were examined: retention, urine toxicology and abstinence. We weighted the RCT sample treatment effects using propensity scores representing the conditional probability of participating in RCTs. Findings Weighting the samples changed the significance of estimated sample treatment effects. Most commonly, positive effects of trials became statistically non-significant after weighting (three trials for retention and urine toxicology and one trial for abstinence); also, non-significant effects became significantly positive (one trial for abstinence) and significantly negative effects became non-significant (two trials for abstinence). There was suggestive evidence of treatment effect heterogeneity in subgroups that are under- or over-represented in the trials, some of which were consistent with the differences in average treatment effects between weighted and unweighted results. Conclusions The findings of randomized controlled trials (RCTs) for substance use disorder treatment do not appear to be directly generalizable to target populations when the RCT samples do not reflect adequately the target populations and there is treatment effect heterogeneity across patient subgroups. [ABSTRACT FROM AUTHOR]

Background: Investigators conducting randomized clinical trials often explore treatment effect heterogeneity to assess whether treatment efficacy varies according to patient characteristics. Identifying heterogeneity is central to making informed personalized healthcare decisions. Treatment effect heterogeneity can be investigated using subpopulation treatment effect pattern plot (STEPP), a non-parametric graphical approach that constructs overlapping patient subpopulations with varying values of a characteristic. Procedures for statistical testing using subpopulation treatment effect pattern plot when the endpoint of interest is survival remain an area of active investigation. Methods: A STEPP analysis was used to explore patterns of absolute and relative treatment effects for varying levels of a breast cancer biomarker, Ki-67, in the phase III Breast International Group 1-98 randomized clinical trial, comparing letrozole to tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor–positive breast cancer. Absolute treatment effects were measured by differences in 4-year cumulative incidence of breast cancer recurrence, while relative effects were measured by the subdistribution hazard ratio in the presence of competing risks using O–E (observed-minus-expected) methodology, an intuitive non-parametric method. While estimation of hazard ratio values based on O–E methodology has been shown, a similar development for the subdistribution hazard ratio has not. Furthermore, we observed that the subpopulation treatment effect pattern plot analysis may not produce results, even with 100 patients within each subpopulation. After further investigation through simulation studies, we observed inflation of the type I error rate of the traditional test statistic and sometimes singular variance–covariance matrix estimates that may lead to results not being produced. This is due to the lack of sufficient number of events within the subpopulations, which we refer to as instability of the subpopulation treatment effect pattern plot analysis. We introduce methodology designed to improve stability of the subpopulation treatment effect pattern plot analysis and generalize O–E methodology to the competing risks setting. Simulation studies were designed to assess the type I error rate of the tests for a variety of treatment effect measures, including subdistribution hazard ratio based on O–E estimation. This subpopulation treatment effect pattern plot methodology and standard regression modeling were used to evaluate heterogeneity of Ki-67 in the Breast International Group 1-98 randomized clinical trial. Results: We introduce methodology that generalizes O–E methodology to the competing risks setting and that improves stability of the STEPP analysis by pre-specifying the number of events across subpopulations while controlling the type I error rate. The subpopulation treatment effect pattern plot analysis of the Breast International Group 1-98 randomized clinical trial showed that patients with high Ki-67 percentages may benefit most from letrozole, while heterogeneity was not detected using standard regression modeling. Conclusion: The STEPP methodology can be used to study complex patterns of treatment effect heterogeneity, as illustrated in the Breast International Group 1-98 randomized clinical trial. For the subpopulation treatment effect pattern plot analysis, we recommend a minimum of 20 events within each subpopulation. [ABSTRACT FROM AUTHOR]

Background: Recent proposals suggest that risk-stratified analyses of clinical trials be routinely performed to better enable tailoring of treatment decisions to individuals. Trial data can be stratified using externally developed risk models (eg, Framingham risk score), but such models are not always available. We sought to determine whether internally developed risk models, developed directly on trial data, introduce bias compared with external models.Methods and Results: We simulated a large patient population with known risk factors and outcomes. Clinical trials were then simulated by repeatedly drawing from the patient population assuming a specified relative treatment effect in the experimental arm, which either did or did not vary according to a subject's baseline risk. For each simulated trial, 2 internal risk models were developed on either the control population only (internal controls only) or the whole trial population blinded to treatment (internal whole trial). Bias was estimated for the internal models by comparing treatment effect predictions to predictions from the external model. Under all treatment assumptions, internal models introduced only modest bias compared with external models. The magnitude of these biases was slightly smaller for internal whole trial models than for internal controls only models. Internal whole trial models were also slightly less sensitive to bias introduced by overfitting and less sensitive to falsely identifying the existence of variability in treatment effect across the risk spectrum compared with internal controls only models.Conclusions: Appropriately developed internal models produce relatively unbiased estimates of treatment effect across the spectrum of risk. When estimating treatment effect, internally developed risk models using both treatment arms should, in general, be preferred to models developed on the control population. [ABSTRACT FROM AUTHOR]

Objective: To determine whether investigations of heterogeneity of treatment effects (HTE) in randomized-controlled trials (RCTs) are prespecified and whether authors' interpretations of their analyses are consistent with the objective evidence.Data Sources/study Setting: Trials published in Annals of Internal Medicine, British Medical Journal, Journal of the American Medical Association, Lancet, and New England Journal of Medicine in 1994, 1999, and 2004.Study Design: We reviewed 87 RCTs that reported formal tests for statistical interaction or heterogeneity (HTE analyses), derived from a probability sample of 541 articles.Data Collection/extraction: We recorded reasons for performing HTE analysis; an objective classification of evidence for HTE (termed "clinicostatistical divergence" [CSD]); and authors' interpretations of findings. Authors' interpretations, compared with CSD, were coded as understated, overstated, or adequately stated.Principle Findings: Fifty-three RCTs (61 percent) claimed prespecified covariates for HTE analyses. Trials showed strong (6), moderate (11), weak (25), or negligible (16) evidence for CSD (29 could not be classified due to inadequate information). Authors stated that evidence for HTE was sufficient to support differential treatment in subgroups (10); warranted more research (31); was absent (21); or provided no interpretation (25). HTE was overstated in 22 trials, adequately stated in 57 trials, and understated in 8 trials.Conclusions: Inconsistencies in performance and reporting may limit the potential of HTE analysis as a tool for identifying HTE and individualizing care in diverse populations. Recommendations for future studies on the reporting and interpretation of HTE analyses are provided. [ABSTRACT FROM AUTHOR]