Many large-scale phase III trials with recurrent event endpoints include a pre-planned interim analysis to evaluate early futility. Conditional power (CP), which quantifies the probability of achieving statistical significance at the final analysis given the interim data, is a commonly used tool to support such decisions. The standard negative binomial model with an offset term, widely adopted for analyzing recurrent events, implicitly assumes that event rates and treatment effects remain constant over the study period. At the interim analysis, however, a substantial proportion of patients have incomplete follow-up, and when the treatment effect is delayed in onset or diminishes over time, the constant-rate assumption introduces systematic bias into the interim estimate and can lead to incorrect futility decisions. In this paper, we propose FLEX-CP-DT, a piecewise negative binomial framework that captures temporal trends in both event rates and treatment effects without imposing the constant-rate assumption. The framework yields a formula-based conditional power calculation that does not require resampling or trial simulation at the interim stage. Through extensive simulations spanning constant-effect and delayed-onset scenarios, we demonstrate that FLEX-CP-DT performs comparably to the standard approach when the constant-rate assumption holds and improves interim futility decision-making when it is violated. A case study calibrated to a published phase 3 bronchiectasis trial further illustrates the practical advantage of the proposed method in reducing the probability of falsely terminating an efficacious drug with delayed treatment onset.

BACKGROUND:Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol-related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero-inflated, particularly compared with recently developed marginalized count regression approaches for such data. METHODS:The current simulation study examined five commonly used approaches for analyzing count outcomes, including two linear models (with outcomes on raw and log-transformed scales, respectively) and three prevailing count distribution-based models (ie, Poisson, negative binomial, and zero-inflated Poisson (ZIP) models). We also considered the marginalized zero-inflated Poisson (MZIP) model, a novel alternative that estimates the overall effects on the population mean while adjusting for zero-inflation. Motivated by alcohol misuse prevention trials, extensive simulations were conducted to evaluate and compare the statistical power and Type I error rate of the statistical models and approaches across data conditions that varied in sample size ( N = 100 $$ N=100 $$ to 500), zero rate (0.2 to 0.8), and intervention effect sizes. RESULTS:Under zero-inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non-zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on the raw scale, negative binomial model, and ZIP model. The performance of the linear model with a log-transformed outcome variable was unsatisfactory. CONCLUSIONS:The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero-inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros.

Severe, protracted symptoms are associated with poor outcomes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In a placebo-controlled study of casirivimab and imdevimab (CAS + IMD) in persons at high risk of severe coronavirus disease 2019 (COVID-19; n = 3816), evolution of individual symptoms was assessed for resolution patterns across risk factors, and baseline SARS-CoV-2-specific antibody responses against S1 and N domains. CAS + IMD versus placebo provided statistically significant resolution for 17/23 symptoms, with greater response linked to absence of endogenous anti–SARS-CoV-2 immunoglobulin (Ig)G, IgA, or specific neutralizing antibodies at baseline, or high baseline viral load. Resolution of five key symptoms (onset days 3–5)—dyspnea, cough, feeling feverish, fatigue, and loss of appetite—independently correlated with reduced hospitalization and death (hazard ratio range: 0.31–0.56; P < 0.001–0.043), and was more rapid in CAS + IMD-treated patients lacking robust early antibody responses. Those who seroconverted late still benefited from treatment. Thus, highly neutralizing COVID-19-specific antibodies provided by CAS + IMD treatment accelerated key symptom resolution associated with hospitalization and death in those at high risk for severe disease as well as in those lacking early, endogenous neutralizing antibody responses.

Improving health and delaying aging is the focus of medical research. Previous studies have shown that mesenchymal stem cell (MSC) senescence is closely related to organic aging and the development of aging-related diseases such as osteoarthritis (OA). m6A is a common RNA modification that plays an important role in regulating cell biological functions, and ALKBH5 is one of the key m6A demethylases. However, the role of m6A and ALKBH5 in MSC senescence is still unclear. Here, we found that the m6A level was enhanced and ALKBH5 expression was decreased in aging MSCs induced by multiple replications, H2O2 stimulation or UV irradiation. Downregulation of ALKBH5 expression facilitated MSC senescence by enhancing the stability of CYP1B1 mRNA and inducing mitochondrial dysfunction. In addition, IGF2BP1 was identified as the m6A reader restraining the degradation of m6A-modified CYP1B1 mRNA. Furthermore, Alkbh5 knockout in MSCs aggravated spontaneous OA in mice, and overexpression of Alkbh5 improved the efficacy of MSCs in OA. Overall, this study revealed a novel mechanism of m6A in MSC senescence and identified promising targets to protect against aging and OA.

During the aging process, the reduced osteogenic differentiation of bone marrow mesenchymal stem cells (BM-MSCs) results in decreased bone formation, which contributes to senile osteoporosis. Previous studies have confirmed that interrupted circadian rhythm plays an indispensable role in age-related disease. However, the mechanism underlying the impaired osteogenic differentiation of BM-MSCs during aging and its relationship with interrupted circadian rhythm remains unclear. In this study, we confirmed that the circadian rhythm was interrupted in aging mouse skeletal systems. The level of the core rhythm component BMAL1 but not that of CLOCK in the osteoblast lineage was decreased in senile osteoporotic specimens from both human and mouse. BMAL1 targeted TTK as a circadian-controlled gene to phosphorylate MDM2 and regulate H2Bub1 level, while H2Bub1 in turn regulated the expression of BMAL1. The osteogenic capacity of BM-MSCs was maintained by a positive loop formed by BMAL1-TTK-MDM2-H2Bub1. Furthermore, we demonstrated that using bone-targeting recombinant adeno-associated virus 9 (rAAV9) to enhance Bmal1 or Ttk might have a therapeutic effect on senile osteoporosis and delays bone repair in aging mice. In summary, our study indicated that targeting the BMAL1-TTK-MDM2-H2Bub1 axis via bone-targeting rAAV9 might be a promising strategy for the treatment of senile osteoporosis.

Background: Atopic march refers to the sequential development of allergic diseases from infancy through adolescence, typically beginning with atopic dermatitis (AD), followed by food allergy and then airway diseases, later evolving to broader or worsened spectrum of allergic diatheses. No intervention has shown to alter its course.Objective: We sought to determine the rate of acquisition of new or worsened allergic events for dupilumab versus placebo in patients with AD. Methods: Allergy-associated events from 12 clinical trials were grouped into 17 allergy categories, and IgE changes from baseline were defined. A new/worsened event was considered one step of atopic march. Treatment effect was assessed by incidence rate ratios (IRRs), dupilumab versus placebo, by meta-analysis.Results: The duration of pooled AD studies was 4 to 52 weeks (1359 patient-years; n = 2296 dupilumab, n = 1229 placebo, median age 35 years). The median age at AD onset was 2 years. Baseline allergic disease burden was comparable between groups. Dupilumab reduced the risk of new/worsening allergies by 34% (IRR 0.66; 95% confidence interval [CI], 0.52-0.84) and new allergies by 37% (IRR 0.63; 95% CI, 0.48-0.83) versus placebo. Including IgE category shift, the IRR for combined new/ worsening allergies was reduced by 54% (IRR 0.46; 95% CI, 0.36-0.57). These treatment benefits did not reverse on treatment discontinuation in off-treatment follow-up.Conclusions: The acquisition/worsening of allergic conditions suggestive of atopic march was observed in a pooled adult/ adolescent AD study population with inadequately controlled AD. Treatment with dupilumab reduced new/worsened allergy events versus placebo; inclusion of IgE category change increased the apparent benefit. (J Allergy Clin Immunol 2023;151:756-66.)

Sustained inflammatory invasion leads to joint damage and progressive disability in several autoimmune rheumatic diseases. In recent decades, targeting M1 macrophage polarization has been suggested as a promising therapeutic strategy for autoimmune arthritis. P300/CBP-associated factor (PCAF) is a histone acetyltransferase (HAT) that exhibits a strong positive relationship with the proinflammatory microenvironment. However, whether PCAF mediates M1 macrophage polarization remains poorly studied, and whether targeting PCAF can protect against autoimmune arthritis in vivo remains unclear. Commonly used drugs can cause serious side effects in patients because of their extensive and nonspecific distribution in the human body. One strategy for overcoming this challenge is to develop drug nanocarriers that target the drug to desirable regions and reduce the fraction of drug that reaches undesirable targets. In this study, we demonstrated that PCAF inhibition could effectively inhibit M1 polarization and alleviate arthritis in mice with collagen-induced arthritis (CIA) via synergistic NF-κB and H3K9Ac blockade. We further designed dextran sulfate (DS)-based nanoparticles (DSNPs) carrying garcinol (a PCAF inhibitor) to specifically target M1 macrophages in inflamed joints of the CIA mouse model via SR-A–SR-A ligand interactions. Compared to free garcinol, garcinol-loaded DSNPs selectively targeted M1 macrophages in inflamed joints and significantly improved therapeutic efficacy in vivo. In summary, our study indicates that targeted PCAF inhibition with nanoparticles might be a promising strategy for treating autoimmune arthritis via M1 macrophage polarization inhibition.

Cluster randomized trials (CRT) have been widely employed in medical and public health research. Many clinical count outcomes, such as the number of falls in nursing homes, exhibit excessive zero values. In the presence of zero inflation, traditional power analysis methods for count data based on Poisson or negative binomial distribution may be inadequate. In this study, we present a sample size method for CRTs with zero-inflated count outcomes. It is developed based on GEE regression directly modeling the marginal mean of a zero-inflated Poisson outcome, which avoids the challenge of testing two intervention effects under traditional modeling approaches. A closed-form sample size formula is derived which properly accounts for zero inflation, ICCs due to clustering, unbalanced randomization, and variability in cluster size. Robust approaches, including t-distribution-based approximation and Jackknife re-sampling variance estimator, are employed to enhance trial properties under small sample sizes. Extensive simulations are conducted to evaluate the performance of the proposed method. An application example is presented in a real clinical trial setting.

INTRODUCTION:Myogenic differentiation plays an important role in pathophysiological processes including muscle injury and regeneration, as well as muscle atrophy. A novel type of posttranslational modification, crotonylation, has been reported to play a role in stem cell differentiation and disease. However, the role of crotonylation in myogenic differentiation has not been clarified.OBJECTIVES:This study aims to find the role of crotonylation during myogenic differentiation and explore whether it is a potential target in myogenic dysfunction disease.METHODS:C2C12 cell line and skeletal muscle mesenchymal progenitors of Mus musculus were used for myogenic process study in vitro, while muscle injury model of mice was used for in vivo muscle regeneration study. Mass spectrometry favored in discovery of potential target protein of crotonylation and its specific sites.RESULTS:We confirmed the gradual decrease in total protein crotonylation level during muscle differentiation and found decreased crotonylation of AKT1, which facilitated an increase in AKT1 phosphorylation. Then we verified that crotonylation of AKT1 at specific sites weakened its binding with PDK1 and impaired its phosphorylation. In addition, we found that increased expression of the crotonylation eraser HDAC3 decreased AKT1 crotonylation levels during myogenic differentiation, jointly promoting myogenic differentiation.CONCLUSION:Our study highlights the important role of decrotonylation of AKT1 in the process of muscle differentiation, where it aids the phosphorylation and activation of AKT1 and promotes myogenic differentiation. This is of great significance for exploring the pathophysiological process of muscle injury repair and sarcopenia.

To evaluate and optimize brief alcohol interventions (BAIs), it is critical to have a credible overall effect size estimate as a benchmark. Estimating such an effect size has been challenging because alcohol outcomes often represent responses from a mixture of individuals: those at high risk for alcohol misuse, occasional nondrinkers, and abstainers. Moreover, some BAIs exclusively focus on heavy drinkers, whereas others take a universal prevention approach. Depending on sample characteristics, the outcome distribution might have many zeros or very few zeros and overdispersion; consequently, the most appropriate statistical model may differ across studies. We synthesized individual participant data (IPD) from 19 studies in Project INTEGRATE (Mun et al., 2015b) that randomly allocated participants to intervention and control groups (N = 7,704 participants, 38.4% men, 74.7% White, 58.5% first-year students). We sequentially estimated marginalized zero-inflated Poisson (Long et al., 2014) or negative binomial regression models to obtain covariate-adjusted, study-specific intervention effect estimates in the first step, which were subsequently combined in a random-effects meta-analysis model in the second step. BAIs produced a statistically significant 8% advantage in the mean number of drinks at both 1-3 months (RR = 0.92, 95% CI = [0.85, 0.98]) and 6 months (RR = 0.92, 95% CI = [0.85, 0.99]) compared to controls. At 9-12 months, there was no statistically significant difference in the mean number of drinks between BAIs and controls. In conclusion, BAIs are effective at reducing the mean number of drinks through at least 6 months post intervention. IPD can play a critical role in deriving findings that could not be obtained in original individual studies or standard aggregate data meta-analyses.

Background: Oridonin is the core bioactive component of Rabdosia rubescens, a traditional Chinese herbal medicine used in the treatment of hepatoma. Sorafenib, a targeted therapeutic agent for advanced hepatocellular carcinoma (HCC), has recently been shown to exert limited clinical effects. However, few studies have focused on the synergistic effect of these two drugs on hepatocellular carcinoma. Methods: We treated different HCC cell lines with different concentrations of oridonin and sorafenib and assessed the viability by using MTT assays and examined proliferation, migration, invasion and apoptosis after cotreatment of HepG2 cells with 20 mu M oridonin and 5 mu M sorafenib via colony formation assays, Transwell assays and flow cytometry. Regulatory effects were measured by Western blotting. The in vivo synergistic effect was confirmed through xenograft tumor models, and tumor tissues were analyzed by immunohistochemistry. Results: The inhibitory effects of oridonin and sorafenib cotreatment on HCC cells were stronger than those of either drug alone. In addition, combined treatment with the two drugs synergistically inhibited epithelial-mesenchymal transition and the Akt pathway but not NF kappa B or MAPK signaling. Akt phosphorylation by SC79 reversed the inhibitory effects of the combined treatment. Synergistic inhibition was equally observed in vivo. Conclusion: Oridonin combined with sorafenib synergistically inhibited proliferation, migration, invasion, and epithelial-mesenchymal transition and induced apoptosis by targeting the Akt pathway but not NF-kappa B or MAPK signaling.

Cluster randomized designs are frequently employed in pragmatic clinical trials which test interventions in the full spectrum of everyday clinical settings in order to maximize applicability and generalizability. In this study, we propose to directly incorporate pragmatic features into power analysis for cluster randomized trials with count outcomes. The pragmatic features considered include arbitrary randomization ratio, overdispersion, random variability in cluster size, and unequal lengths of follow‐up over which the count outcome is measured. The proposed method is developed based on generalized estimating equation (GEE) and it is advantageous in that the sample size formula retains a closed form, facilitating its implementation in pragmatic trials. We theoretically explore the impact of various pragmatic features on sample size requirements. An efficient Jackknife algorithm is presented to address the problem of underestimated variance by the GEE sandwich estimator when the number of clusters is small. We assess the performance of the proposed sample size method through extensive simulation and an application example to a real clinical trial is presented.

Background: Bisdemethoxycurcumin (BDMC), a stable bioactive ingredient in curcuminoids, is associated with various antitumor functions, such as proliferation inhibition, metastasis suppression and apoptosis induction, in many cancer types. However, the mechanism of BDMC in hepatocellular carcinoma (HCC) remains unclear. Methods: We assessed the toxicity and the inhibitory effect of BDMC in the HepG2 cell line by using CCK-8 and colony formation assays. The regulatory effects of BDMC on Akt and MAPK signaling were investigated by Western blotting and immunoprecipitation. Results: We found that the half-maximum inhibitory concentration (IC50) of BDMC after 48 hrs of treatment was 59.13 mu M, and BDMC inhibited proliferation in a time- and dose-dependent manner in HepG2 cells. The inhibitory effect was caused by the inactivation of Akt signaling, but not Erk, Jnk or p38 signaling. In addition, the inactivation of Akt signaling was attributed to the inhibition of ubiquitination mediated by K63-Ub but not K48-Ub. Furthermore, we found that BDMC upregulated the expression of CYLD, leading to Akt deubiquitination and inactivation. Conclusion: BDMC inhibited HCC cell proliferation, and that this effect was induced by Akt inactivation via CYLD-mediated deubiquitination.

Chengjiang Qiu Kairui Liu Sheng Zhang Simin Gao Weirun Chen Dateng Li Youxing Huang 1Department of Abdominal Surgery, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, People’s Republic of China; 2Department of Breast Surgery, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China; 3Department of Statistical Science, Southern Methodist University, Dallas, TX 75275, USA Background: Bisdemethoxycurcumin (BDMC), a stable bioactive ingredient in curcuminoids, is associated with various antitumor functions, such as proliferation inhibition, metastasis suppression and apoptosis induction, in many cancer types. However, the mechanism of BDMC in hepatocellular carcinoma (HCC) remains unclear. Methods: We assessed the toxicity and the inhibitory effect of BDMC in the HepG2 cell line by using CCK-8 and colony formation assays. The regulatory effects of BDMC on Akt and MAPK signaling were investigated by Western blotting and immunoprecipitation. Results: We found that the half-maximum inhibitory concentration (IC50) of BDMC after 48 hrs of treatment was 59.13 μM, and BDMC inhibited proliferation in a timeand dosedependent manner in HepG2 cells. The inhibitory effect was caused by the inactivation of Akt signaling, but not Erk, Jnk or p38 signaling. In addition, the inactivation of Akt signaling was attributed to the inhibition of ubiquitination mediated by K63-Ub but not K48-Ub. Furthermore, we found that BDMC upregulated the expression of CYLD, leading to Akt deubiquitination and inactivation. Conclusion: BDMC inhibited HCC cell proliferation, and that this effect was induced by Akt inactivation via CYLD-mediated deubiquitination.

Cluster randomized trials (CRTs) are widely used in different areas of medicine and public health. Recently, with increasing complexity of medical therapies and technological advances in monitoring multiple outcomes, many clinical trials attempt to evaluate multiple co-primary endpoints. In this study, we present a power analysis method for CRTs with K≥2 binary co-primary endpoints. It is developed based on the GEE (generalized estimating equation) approach, and three types of correlations are considered: inter-subject correlation within each endpoint, intra-subject correlation across endpoints, and inter-subject correlation across endpoints. A closed-form joint distribution of the K test statistics is derived, which facilitates the evaluation of power and type I error for arbitrarily constructed hypotheses. We further present a theorem that characterizes the relationship between various correlations and testing power. We assess the performance of the proposed power analysis method based on extensive simulation studies. An application example to a real clinical trial is presented.

Statistical inference based on correlated count measurements are frequently performed in biomedical studies. Most of existing sample size calculation methods for count outcomes are developed under the Poisson model. Deviation from the Poisson assumption (equality of mean and variance) has been widely documented in practice, which indicates urgent needs of sample size methods with more realistic assumptions to ensure valid experimental design. In this study, we investigate sample size calculation for clinical trials with correlated count measurements based on the negative binomial distribution. This approach is flexible to accommodate overdispersion and unequal measurement intervals, as well as arbitrary randomization ratios, missing data patterns, and correlation structures. Importantly, the derived sample size formulas have closed forms both for the comparison of slopes and for the comparison of time‐averaged responses, which greatly reduces the burden of implementation in practice. We conducted extensive simulation to demonstrate that the proposed method maintains the nominal levels of power and type I error over a wide range of design configurations. We illustrate the application of this approach using a real epileptic trial.

Over-dispersed count variables are frequently encountered in biomedical research. Despite extensive research in analytical methods, addressing over-dispersion in the design of clinical trials has received much less attention. In this study, we propose to directly incorporate over-dispersion into sample size calculation for clinical trials where a count outcome is repeatedly measured on each subject. The proposed method is applicable to the comparison of slopes as well as time-averaged responses. It is easy to compute and flexible enough to account for unbalanced randomization, arbitrary missing patterns, and different correlation structures. We show that sample size requirement is proportional to over-dispersion, which highlights the danger of ignoring over-dispersion in experimental design. Simulation results demonstrate that the proposed sample size calculation methods maintain the nominal levels of power and Type I error over a wide range of scenarios. Application example to an epileptic trial is presented. Supplementary materials for this article are available online.

Vibratory function of the vocal folds is largely determined by the rheological properties or viscoelastic shear properties of the vocal fold lamina propria. To date, investigation of the sample size estimation and statistical experimental design for vocal fold rheological studies is nonexistent. The current work provides the closed-form sample size formulas for two major study designs (i.e. paired and two-group designs) in vocal fold research. Our results demonstrated that the paired design could greatly increase the statistical power compared to the two-group design. By comparing the variance of estimated treatment effect, this study also confirms that ignoring within-subject and within-vocal fold correlations during rheological data analysis will likely increase type I errors. Finally, viscoelastic shear properties of intact and scarred rabbit vocal fold lamina propria were measured and used to illustrate theoretical findings in a realistic scenario and project sample size requirement for future studies.