Exploring the mechanism of action of Shuangyang houbitong granules in the treatment of acute pharyngitis based on network pharmacology and molecular docking.

BACKGROUND: Acute pharyngitis (AP) refers to the acute inflammation of the pharynx, characterized by swelling and pain in the throat. Shuangyang houbitong granules (SHG), a traditional Chinese medicine compound, have been found to be effective in providing relief from symptoms associated with AP. METHODS: The chemical components of SHG were screened using Traditional Chinese Medicine Systems Pharmacology database, HERB database, and China National Knowledge Infrastructure. The targets of the granules were predicted using SwissTargetPrediction database. A network was constructed based on the targets of AP obtained from Genecards database, and protein-protein interaction analysis was performed on the intersection targets using STRING database. Key targets were screened for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and the binding activity of components and targets was predicted using AutoDockTools-1.5.7. RESULTS: A total of 65 components of SHG that met the screening criteria were retrieved, resulting in 867 corresponding targets. Additionally, 1086 AP target genes were retrieved, and 272 gene targets were obtained from the intersection as potential targets for SHG in the treatment of AP. Molecular docking results showed that the core components genkwanin, acacetin, apigenin, quercetin can stably bind to the core targets glyceraldehyde 3-phosphate dehydrogenase, interleukin 6, tumor necrosis factor, serine/threonine protein kinase, tumor protein 53, and epidermal growth factor receptor. CONCLUSION: The research results preliminarily predict and verify the mechanism of action of SHG in the treatment of AP, providing insights for further in-depth research.

Protracted molecular dynamics and secondary structure introspection to identify dual-target inhibitors of Nipah virus exerting approved small molecules repurposing.

Nipah virus (NiV), with its significantly higher mortality rate compared to COVID-19, presents a looming threat as a potential next pandemic, particularly if constant mutations of NiV increase its transmissibility and transmission. Considering the importance of preventing the facilitation of the virus entry into host cells averting the process of assembly forming the viral envelope, and encapsulating the nucleocapsid, it is crucial to take the Nipah attachment glycoprotein-human ephrin-B2 and matrix protein as dual targets. Repurposing approved small molecules in drug development is a strategic choice, as it leverages molecules with known safety profiles, accelerating the path to finding effective treatments against NiV. The approved small molecules from DrugBank were used for repurposing and were subjected to extra precision docking followed by absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling. The 4 best molecules were selected for 500 ns molecular dynamics (MD) simulation followed by Molecular mechanics with generalized Born and surface area solvation (MM-GBSA). Further, the free energy landscape, the principal component analysis followed by the defined secondary structure of proteins analysis were introspected. The inclusive analysis proposed that Iotrolan (DB09487) and Iodixanol (DB01249) are effective dual inhibitors, while Rutin (DB01698) and Lactitol (DB12942) were found to actively target the matrix protein only.

Systematic review and meta-analysis on the efficacy and safety of Injectable Lentinan combined with chemotherapy in the treatment of gastric cancer.

BACKGROUND: This study employed a meta-analysis to evaluate the efficacy and safety of adjunctive treatment with injectable Lentinan (LNT) in combination with chemotherapy for gastric cancer (GC). METHODS: Computer-based searches of 6 databases were performed to identify randomized controlled trials (RCTs) relevant to the treatment of GC with LNT through mid-March 2023. Two independent researchers performed risk of bias assessment and trial sequential analysis(TSA), extracted the data and used Revman 5.3 software for data analysis. The certainty of evidence was graded based on the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. RESULTS: A total of 31 RCTs with 2729 patients were included in the analysis. The results revealed that adjunctive therapy with LNT was associated with improved treatment efficacy (RR = 1.48, 95%CI: 1.36 ∼ 1.61, p < 0.00001), improvement in clusters of differentiation (CD3+, CD4+, and CD4+/CD8+), natural killer (NK) cells, and quality of life assessment (RR = 1.32, 95%CI: 1.20 ∼ 1.45, p < 0.00001) compared to using chemotherapy alone. In addition, there was a reduction in CD8+ levels, incidence of white blood cell decline, gastrointestinal reactions, and platelet decline. TSA results indicated that there was sufficient evidence to draw firm conclusions about these outcomes, and the GRADE scores showed 'high' or 'moderate' quality of evidence for these outcomes. CONCLUSION: The efficacy of treatment of GC with LNT in combination with chemotherapy was found to be better than chemotherapy alone. And no serious adverse effects were observed. However, further RCTs are needed to further validate the results of this study.

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes.

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

Subtle distinction in molecular structure of flavonoids leads to vastly different coating efficiency and mechanism of metal-polyphenol networks with excellent antioxidant activities.

Metal-polyphenol networks (MPNs) are of immense scientific interest because of their simple and rapid process to deposit on various substrates or particles with different shapes. However, there are rare reports on the effect of polyphenol molecular structure on coating efficiency and mechanism of MPNs. Herein, three typical flavonoid polyphenols, catechin (Cat), epigallocatechin (EGC) and procyanidin (PC), with the same skeleton (C6-C3-C6) but subtle distinction in molecular structure, were selected to build MPN coatings with ferric ions (Fe3+). And various techniques combined with the density functional theory (DFT) were applied to deeply reveal the roles of coordinative phenolic hydroxyl groups as well as noncovalent interactions (hydrogen bonding and π - π stacking) in the formation of flavonoid-based MPNs. We found that more accessible numbers of coordinative phenolic hydroxyl groups, the higher coating efficiency. In these flavonoid-based MPNs, the single-complex is the predominant during the coordinative modes between phenolic hydroxyl and Fe3+, not the previously reported mono-complex, bis-complex and/or tris-complex. Besides coordinative interaction, noncovalent interactions also contribute to MPNs formation, and hydrogen bonds prevail in the noncovalent interaction compared with π-π stacking. And these engineered MPN coatings can endow the substrate with excellent antioxidant activities. This study contributes to in-depth understanding the building mechanism of flavonoid-based MPNs, and increasing coating efficiency by choosing proper polyphenols.

Computer-assisted identification of potential quinolone derivatives targeting Nipah virus glycoprotein attachment with human cell surface receptor ephrin-B2: Multistep virtual screening.

Nipah Virus (NiV) is a single-stranded, negative-sense, highly lethal RNA virus. Even though NiV has close to 70-80% of mortality in India and Bangladesh, still there is no available US FDA-approved drug or vaccine. NiV attachment glycoprotein (NiV-G) is critical for NiV to invade the human cell where ephrinB2 which is a crucial membrane-bound ligand that acts as a target of NiV. Most of the research has been performed targeting NiV or human ephrin-B to date. Quinolone derivatives are proven scaffolds for many approved drugs used to treat various bacterial, viral respiratory tract, and urinary tract infections, and rheumatologic disorders such as systemic lupus erythematosus, rheumatoid arthritis. Therefore, we have tried to find potential drug molecules employing quinolone scaffold-based derivatives from PubChem targeting both NiV-G and ephrin-B2 protein. A total of 1500+ quinolone derivatives were obtained from PubChem which were screened based on Drug Likeness followed by being subjected to XP docking employing Schrödinger software. The top ten best molecules were then chosen for their absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling based on the docking score ranking. Further, the top five molecules were selected for 200ns molecular dynamics (MD) simulation study with Desmond module followed by MM-GBSA study by Prime module of Schrödinger. The exhaustive analysis leads us to the top three probable lead drug molecules for NiV are PubChem CID 23646770, an analog of PubChem CID 67726448, and PubChem CID 10613168 which have predicted Ki values of 0.480 µm, 0.785 µm, and 0.380 µm, respectively. These proposed molecules can be the future drugs targeting NiV-G and human ephrin-B2 which requires further in vivo validation.

Are we ready to fight the Nipah virus pandemic? An overview of drug targets, current medications, and potential leads.

Nipah virus (NiV) is a high-lethality RNA virus from the family of Paramyxoviridae and genus Henipavirus, classified under Biosafety Level-4 (BSL-4) pathogen due to the severity of pathogenicity and lack of medications and vaccines. Direct contacts or the body fluids of infected animals are the major factor of transmission of NiV. As it is not an airborne infection, the transmission rate is relatively low. Still, mutations of the NiV in the animal reservoir over the years, followed by zoonotic transfer, can make the deadliness of the virus manifold in upcoming years. Therefore, there is no denial of the possibility of a pandemic after COVID-19 considering the severe pathogenicity of NiV, and that is why we need to be prepared with possible drugs in upcoming days. Considering the time constraints, computational aided drug design (CADD) is an efficient way to study the virus and perform the drug design and test the HITs to lead experimentally. Therefore, this review focuses primarily on NiV target proteins (covering NiV and human), experimentally tested repurposed drug details, and latest computational studies on potential lead molecules, which can be explored as potential drug candidates. Computationally identified drug candidates, including their chemical structures, docking scores, amino acid level interaction with corresponding protein, and the platform used for the studies, are thoroughly discussed. The review will offer a one-stop study to access what had been performed and what can be performed in the CADD of NiV.

Dexamethasone for Inpatients With COVID-19 in a National Cohort.

Importance: Limited effective therapeutics are available to hospitalized patients with COVID-19. Clinical trials and observational studies have shown varying effects of systemic corticosteroids, including dexamethasone, in hospitalized patients with COVID-19, with limited descriptions of important patient subgroups. Objective: To examine the clinical use of dexamethasone for hospitalized patients with COVID-19 respiratory illness and to explore the heterogeneity of treatment outcomes across different subgroups. Design, Setting, and Participants: This is a retrospective, propensity score-weighted cohort study of adult patients hospitalized for at least 48 hours for COVID-19 respiratory illness between July 1, 2020, and October 31, 2021, at a large health care network of 156 hospitals across the US. Data analysis was performed from March 2022 to February 2023. Exposures: Systemic dexamethasone administered within 48 hours of either admission or escalation in oxygen support. Main Outcomes and Measures: All-cause in-hospital mortality or discharge to hospice. Results: A total of 80 699 patients who met the eligibility criteria were identified (median [IQR] age, 64 [52-76] years; 37 606 women [46.6%]); 13 230 patients (16.4%) identified as Black, 49 222 (60.9%) as White, 18 247 (22.6%) as other race, and 20 340 (25.2%) as Hispanic ethnicity. Of these patients, 13 040 (16.2%) did not require supplemental oxygen within 48 hours of admission, 56 368 (69.8%) required supplemental oxygen, 7618 (9.4%) required noninvasive positive pressure ventilation (NIPPV), and 3673 (4.6%) required mechanical ventilation (MV) and/or extracorporeal membrane oxygenation (ECMO). After adjustment by propensity score overlap weighting, early use of dexamethasone was associated with reduction in a composite outcome of in-hospital mortality or discharge to hospice for patients receiving supplemental oxygen (aOR, 0.92; 95% CI, 0.86-0.98) and MV and/or ECMO (aOR, 0.82; 95% CI, 0.68-0.99). In contrast, all-cause inpatient mortality or discharge to hospice was not lower for patients who received dexamethasone in the no supplemental oxygen group (aOR, 0.90; 95% CI, 0.78-1.03) and in the NIPPV group (aOR, 0.87; 95% CI, 0.73-1.04). Importantly, patients with more comorbidities had greater benefit from dexamethasone use. Conclusions and Relevance: In this national multicenter cohort study of inpatients with COVID-19, early administration of dexamethasone was associated with significantly reduced odds of mortality or discharge to hospice in those requiring supplemental oxygen or MV and/or ECMO but not in those requiring no supplemental oxygen or NIPPV. These results support the continued use of systemic dexamethasone in patients hospitalized with COVID-19.

Simulation-Based Training to Improve Clinical Pharmacist Self-Efficacy in the Management of a Rapidly Decompensating Patient.

Background: The optimal training method to prepare pharmacists as an integral rapid response team or cardiopulmonary arrest responders is poorly described. This study assessed the utility of simulation-based training (SBT) as a training technique for clinical pharmacists. Objective: This study aimed to determine if attending SBT is associated with an improvement in self-efficacy. Methods: This single-center, prospective, interventional cohort study offered three simulations to clinical pharmacists over the course of seven months at a 957-bed quaternary care academic medical center. Pharmacists who participated in at least one simulation were categorized in the intervention group and were compared to pharmacists who did not attend a simulation. All participants were asked to complete a 19-question self-efficacy survey in the form of a 100-point scale, a 15-question multiple-choice knowledge assessment, and a perception survey in the form of 4-point Likert scale administered at baseline and following the conclusion of the SBT. Results: Forty-four clinical pharmacists participated; 20 in the intervention group and 24 in the control group. Median change in self-efficacy score improved significantly in the intervention group compared to the control group (14.3 vs 2.3, P = .009). Median change in perception score improved significantly (2 vs 0, P = .046). Knowledge score did not change significantly from baseline. Conclusion: Simulation-based training improved clinical pharmacist self-efficacy and perceptions in the care of rapidly decompensating patients. These findings support SBT as a viable modality of training clinical pharmacists for the management of rapidly decompensating patients.

Power analysis for cluster randomized trials with continuous coprimary endpoints.

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K ( K ≥ 2 $K\ge 2$ ) binary coprimary endpoints are available for cluster randomized trials (CRTs), to our knowledge, methods for continuous coprimary endpoints are not yet available. Assuming a multivariate linear mixed model (MLMM) that accounts for multiple types of intraclass correlation coefficients among the observations in each cluster, we derive the closed-form joint distribution of K treatment effect estimators to facilitate sample size and power determination with different types of null hypotheses under equal cluster sizes. We characterize the relationship between the power of each test and different types of correlation parameters. We further relax the equal cluster size assumption and approximate the joint distribution of the K treatment effect estimators through the mean and coefficient of variation of cluster sizes. Our simulation studies with a finite number of clusters indicate that the predicted power by our method agrees well with the empirical power, when the parameters in the MLMM are estimated via the expectation-maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.

Chondroitin polymerizing factor predicts a poor prognosis and promotes breast cancer progression via the upstream TGF-ß1/SMAD3 and JNK axis activation.

Aberrant composition of glycans in the tumor microenvironment (TME) contributes to tumor progression and metastasis. Chondroitin polymerizing factor (CHPF) is a glycosyltransferase that catalyzes the biosynthesis of chondroitin sulfate (CS). It is also correlated to transforming growth factor-ß1 (TGF-ß1) expression, a crucial mediator in the interaction of cancer cells with TME. In this study, we investigated the association of CHPF expression with the clinicopathological features of breast cancer (BRCA), as well the oncogenic effect and the underling mechanisms of CHPF upon BRCA cells. We found that CHPF expression is significantly increased in human BRCA tissues, and it is positively associated with TGF-ß expression (r = 0.7125). The high-expression of CHPF predicts a poor prognosis and is positively correlated with tumor mass, lymph node metastasis, clinical staging and HER-2 negative-expression. The mechanistic study revealed that it promotes BRCA cell proliferation, migration and invasion through TGF-ß1-induced SMAD3 and JNK activation in vitro, JNK (SP600125) or SMAD3 (SIS3) inhibitor can remove the promotion of CHPF upon cell proliferation, migration and invasion in MDA-MB-231 cells, which is derived from triple-negative breast cancer (TNBC). Collectively, our finding suggested CHPF may function as an oncogene and is highly expressed in human BRCA tissues. Pharmacological blockade of the upstream of JNK or SMAD3 signaling may provide a novel therapeutic target for refractory TNBC patients with CHPF abnormal high-expression.

HOXD11 upregulates JAM-A and exerts oncogenic properties via NF-κB signaling pathway in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a tumor with high incidence and poor prognosis in developing countries. Junctional adhesion molecule A (JAM-A, also known as F11R) affects numerous biological processes, which is a vital regulator of the development of malignant tumors. However, its exact role and underlying mechanism in ESCC remain obscure. Our present study demonstrated that JAM-A was upregulated in ESCC tissues and cell lines by RNA sequencing and immunohistochemistry (IHC). JAM-A knockdown significantly suppressed the proliferation of the ESCC cells, induced cell cycle arrest at the G1 and promoted apoptosis, and suppressed the ability of invasion and migration in vivo and in vitro. Mechanistically, JAM-A may activate the NF-κB signaling pathway to regulate malignant behavior of ESCC. Further research showed that Homeobox D11 (HOXD11) could directly regulate JAM-A transcription by binding to specific sequences of JAM-A promoter region, thereby activating NF-κB signaling pathway to regulate malignant behavior of ESCC. Functional experiments indicated that HOXD11 could exert an oncogenic role in ESCC. Collectively, our findings support the hypothesis that the HOXD11/JAM-A/NF-κB signal axis plays a role in regulating malignant behavior in ESCC patients, highlighting its potential therapeutic value for ESCC.

Efficacy and Safety of Non-Steroidal Mineralocorticoid Receptor Antagonists in Patients With Chronic Kidney Disease and Type 2 Diabetes: A Systematic Review Incorporating an Indirect Comparisons Meta-Analysis.

Background: The non-steroidal mineralocorticoid receptor antagonists (MRAs) are promising treatments in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). We conducted a meta-analysis to explore the efficacy and safety of the non-steroidal MRAs (finerenone, apararenone, esaxerenone) and detect the differences among them. Methods: We searched several databases for eligible randomized controlled trials (RCTs) investigating non-steroidal MRAs versus placebo in patients with CKD and T2D. We performed a conventional meta-analysis separately, and then indirect comparisons for efficacy and safety outcomes were conducted among these included drugs. Results: Eight RCTs with 14,450 subjects were enrolled. In patients with CKD and T2D, a greater reduction in urinary albumin-to-creatinine ratio (UACR) (WMD -0.40, 95% CI -0.48 to -0.32, p < 0.001), estimated glomerular filtration rate (eGFR) (WMD -2.69, 95% CI -4.47 to -0.91, p = 0.003), systolic blood pressure (SBP) (WMD -4.84, 95% CI -5.96 to -3.72, p < 0.001) and a higher risk of hyperkalemia (RR 2.07, 95% CI 1.86 to 2.30, p < 0.001) were observed in the non-steroidal MRAs versus placebo; there is no significant difference in the incidence of serious adverse events between two groups (RR 1.32, 95% CI 0.98 to 1.79, p = 0.067). Compared with finerenone, esaxerenone showed no significant difference in UACR reduction (WMD 0.24, 95% CI -0.016 to 0.496, p = 0.869); apararenone and esaxerenone showed greater decreases in SBP (WMD 1.37, 95% CI 0.456 to 2.284, p = 0.010; WMD 3.11, 95% CI 0.544 to 5,676, p = 0.021). Conclusions: Despite the moderate increased risk of hyperkalemia, use of non-steroidal MRAs could reduce proteinuria and SBP in patients with CKD and T2D. In terms of renoprotection, esaxerenone and finerenone may have similar effects. Esaxerenone and apararenone may have better antihypertensive effects than finerenone. The head-to-head RCTs are still needed to compare the differences of the efficacy and safety in these non-steroidal MRAs.

Impact of Education and Data Feedback on Guideline-Concordant Prescribing for Urinary Tract Infections in the Outpatient Setting.

BACKGROUND: Urinary tract infections (UTIs) are the most common outpatient indication for antibiotics and an important target for antimicrobial stewardship (AS) activities. With The Joint Commission standards now requiring outpatient AS, data supporting effective strategies are needed. METHODS: We conducted a 2-phase, prospective, quasi-experimental study to estimate the effect of an outpatient AS intervention on guideline-concordant antibiotic prescribing in a primary care (PC) clinic and an urgent care (UC) clinic between August 2017 and July 2019. Phase 1 of the intervention included the development of clinic-specific antibiograms and UTI diagnosis and treatment guidelines, presented during educational sessions with clinic providers. Phase 2, consisting of routine clinic- and provider-specific feedback, began ~12 months after the initial education. The primary outcome was percentage of encounters with first- or second-line antibiotics prescribed according to clinic-specific guidelines and was assessed using an interrupted time series approach. RESULTS: Data were collected on 4724 distinct patients seen during 6318 UTI encounters. The percentage of guideline-concordant prescribing increased by 22% (95% CI, 12% to 32%) after Phase 1 education, but decreased by 0.5% every 2 weeks afterwards (95% CI, -0.9% to 0%). Following routine data feedback in Phase 2, guideline concordance stabilized, and significant further decline was not seen (-0.6%; 95% CI, -1.6% to 0.4%). This shift in prescribing patterns resulted in a 52% decrease in fluoroquinolone use. CONCLUSIONS: Clinicians increased guideline-concordant prescribing, reduced UTI diagnoses, and limited use of high-collateral damage agents following this outpatient AS intervention. Routine data feedback was effective to maintain the response to the initial education.

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

BACKGROUND: The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown. PURPOSE: To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions. MATERIAL AND METHODS: A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II-IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions. RESULTS: The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934-0.979) as well as in the test dataset (AUCs = 0.892-0.962) of cohort B (P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images (P = 0.038) in the training dataset of cohort B. CONCLUSION: No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach.

BACKGROUND: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. METHODS: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. RESULTS: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. CONCLUSION: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate-subgroup interactions in the propensity score model.

Propensity score weighting for causal subgroup analysis.

A common goal in comparative effectiveness research is to estimate treatment effects on prespecified subpopulations of patients. Though widely used in medical research, causal inference methods for such subgroup analysis (SGA) remain underdeveloped, particularly in observational studies. In this article, we develop a suite of analytical methods and visualization tools for causal SGA. First, we introduce the estimand of subgroup weighted average treatment effect and provide the corresponding propensity score weighting estimator. We show that balancing covariates within a subgroup bounds the bias of the estimator of subgroup causal effects. Second, we propose to use the overlap weighting (OW) method to achieve exact balance within subgroups. We further propose a method that combines OW and LASSO, to balance the bias-variance tradeoff in SGA. Finally, we design a new diagnostic graph-the Connect-S plot-for visualizing the subgroup covariate balance. Extensive simulation studies are presented to compare the proposed method with several existing methods. We apply the proposed methods to the patient-centered results for uterine fibroids (COMPARE-UF) registry data to evaluate alternative management options for uterine fibroids for relief of symptoms and quality of life.

Radiological Society of North America/Quantitative Imaging Biomarker Alliance Shear Wave Speed Bias Quantification in Elastic and Viscoelastic Phantoms.

OBJECTIVES: To quantify the bias of shear wave speed (SWS) measurements between different commercial ultrasonic shear elasticity systems and a magnetic resonance elastography (MRE) system in elastic and viscoelastic phantoms. METHODS: Two elastic phantoms, representing healthy through fibrotic liver, were measured with 5 different ultrasound platforms, and 3 viscoelastic phantoms, representing healthy through fibrotic liver tissue, were measured with 12 different ultrasound platforms. Measurements were performed with different systems at different sites, at 3 focal depths, and with different appraisers. The SWS bias across the systems was quantified as a function of the system, site, focal depth, and appraiser. A single MRE research system was also used to characterize these phantoms using discrete frequencies from 60 to 500 Hz. RESULTS: The SWS from different systems had mean difference 95% confidence intervals of ±0.145 m/s (±9.6%) across both elastic phantoms and ± 0.340 m/s (±15.3%) across the viscoelastic phantoms. The focal depth and appraiser were less significant sources of SWS variability than the system and site. Magnetic resonance elastography best matched the ultrasonic SWS in the viscoelastic phantoms using a 140 Hz source but had a - 0.27 ± 0.027-m/s (-12.2% ± 1.2%) bias when using the clinically implemented 60-Hz vibration source. CONCLUSIONS: Shear wave speed reconstruction across different manufacturer systems is more consistent in elastic than viscoelastic phantoms, with a mean difference bias of < ±10% in all cases. Magnetic resonance elastographic measurements in the elastic and viscoelastic phantoms best match the ultrasound systems with a 140-Hz excitation but have a significant negative bias operating at 60 Hz. This study establishes a foundation for meaningful comparison of SWS measurements made with different platforms.

Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials.

Cluster randomized trials (CRTs) refer to experiments with randomization carried out at the cluster or the group level. While numerous statistical methods have been developed for the design and analysis of CRTs, most of the existing methods focused on testing the overall treatment effect across the population characteristics, with few discussions on the differential treatment effect among subpopulations. In addition, the sample size and power requirements for detecting differential treatment effect in CRTs remain unclear, but are helpful for studies planned with such an objective. In this article, we develop a new sample size formula for detecting treatment effect heterogeneity in two-level CRTs for continuous outcomes, continuous or binary covariates measured at cluster or individual level. We also investigate the roles of two intraclass correlation coefficients (ICCs): the adjusted ICC for the outcome of interest and the marginal ICC for the covariate of interest. We further derive a closed-form design effect formula to facilitate the application of the proposed method, and provide extensions to accommodate multiple covariates. Extensive simulations are carried out to validate the proposed formula in finite samples. We find that the empirical power agrees well with the prediction across a range of parameter constellations, when data are analyzed by a linear mixed effects model with a treatment-by-covariate interaction. Finally, we use data from the HF-ACTION study to illustrate the proposed sample size procedure for detecting heterogeneous treatment effects.