Panel count data occurs in a wide variety of applications, ranging from biomedical research to business, such as the number of accidents, product defects, and insurance claims. For such data under the FDA investigation, millions of reported adverse events (AEs) associated with thousands of drugs are monitored in the post-market drug safety surveillance systems worldwide. Evaluating the AEs of the associated drugs is an important public health concern and motivates our method. One statistical challenge in such systems is handling the excessive number of zero AE counts. Most existing methods utilize Poisson count models that cannot incorporate covariates nor account for the excessive zero counts adequately. This article proposes a novel semiparametric nonhomogeneous panel count model to detect AE signals by accounting for covariates, background AE occurrences, and excessive zero counts. The model is estimated using the Expectation-Maximization (EM) algorithm iteratively, where in each M-step, the maximization of the nonparametric component is reformulated as an optimization problem, as in the isotonic regression. The strong consistency and the asymptotic distributions of the estimators are formally derived. We conduct extensive simulation studies to evaluate the finite sample performance of the proposed method and to demonstrate the apparent advantage of the proposed method in signal detection with high power, high specificity, and sensitivity. We apply the method to a VigiBase dataset to detect the AE signals as an application of the proposed method.

In biomedical research, especially in epidemiological studies, we often need to compare nonrandomized groups, as randomization is only sometimes feasible. Even in randomized clinical trials, the actual treatment assignments may not be completely random, or the covariates may be imbalanced between treatment and control groups. It is known that naïve estimates of treatment effects are biased if treatments are not randomized or covariates not balanced. Various causal inference methods have been proposed to debias or correct the bias. The doubly robust (DR) method is a more recent development that aims to ensure the robustness of the causal estimates to model assumptions. However, the DR method may still be significantly biased even when the model is only mildly misspecified. Motivated by the need to increase the robustness of multi-group causal estimates, e.g., treatments in multiarm clinical trials, and the effects of multiple levels of smoking intensity on health outcomes in epidemiological studies, this article proposes a semiparametric doubly robust estimator for multigroup causal effects, in which both the propensity score and the outcome models are specified semiparametrically with link functions estimated by shape-restricted maximum likelihood. The approach is shown to be highly robust against model assumptions, enhancing the double robustness. After deriving the asymptotic properties of the proposed DR estimators, we perform simulations to demonstrate the finite sample properties of the proposed estimators and compare them with parametric and naïve estimators. We then apply the method to analyzing the effect of smoking intensity on health outcomes in the National Epidemiology Follow-up Study.

Head and neck squamous cell carcinoma (HNSCC) significantly impacts patients’ quality of life (QoL). This study investigated long-term QoL outcomes among 619 patients with newly diagnosed HNSCC over five years. QoL was assessed annually using the SF-36, covering physical functioning, emotional well-being, social functioning, pain, and general health. Patients were stratified by tumor subsite and smoking status. Significant improvements in QoL were observed at the 1-year follow-up, particularly among patients with pharyngeal [adjusted β: 3.56 (95

In observational studies, the treatment assignment is typically not random. Even in randomized clinical trials, the randomization may be imperfect given the limitation of sample size. In these cases, traditional statistical methods may lead to biased estimates of treatment effects, and causal inference methods are needed to obtain unbiased estimates. The doubly robust estimator (DRE) is a recent development in causal inference, but the literature on DRE for survival data is very limited, and existing methods tend to have complicated forms and may not have double robustness in the original sense. Some are constructed based on the Nelson-Aalen estimator, and to our knowledge no DRE is constructed based on the Kaplan-Meier estimator. Furthermore, in these methods, the propensity score model is often subjectively specified with a logistic model. DRE can be seriously biased if the propensity score and outcome models are slightly misspecified. Here we propose a new semiparametric robust estimator that utilizes the Kaplan-Meier estimator and Stute weighted empirical form to address these issues. Our proposed estimator is not only doubly robust in the original sense but also enhances robustness with the use of semiparametric specification. The asymptotic properties of the proposed estimator are derived, and extensive simulation studies are conducted to evaluate its finite sample performance and compare it with existing methods. Finally, we apply our proposed method to a real clinical study.

Traditional randomized controlled trials (RCTs) face increasing challenges due to lengthy recruitment and high costs. Regulators have encouraged the use of external data and real-world evidence (RWE) to improve efficiency, yet adoption in confirmatory settings remains limited by concerns over heterogeneity and bias. We conducted a proof-of-concept study to assess the feasibility and regulatory value of a hybrid Bayesian borrowing design to support a Phase III RCT of Dexamethasone Intracameral Drug-Delivery Suspension (DEXYCU) in China. Using the Equivalence Probability Propensity Score Meta-Analytic-Predictive (EQPSMAP) approach, we integrated three data sources-a global RCT, a regional Phase III RCT in China, and a real-world data (RWD) in China. The method's performance was evaluated via point estimates and 95% credible intervals for the primary efficacy endpoint. The hybrid design based on EQPSMAP demonstrated greater robustness and accuracy in the presence of baseline imbalances and heterogeneous data. Compared to a traditional RCT, the hybrid design reduced the required sample size by 41 to 158 patients and shortened trial duration by approximately 2 to 5 months while preserving internal validity. This study demonstrated the feasibility and regulatory value of hybrid Bayesian designs in late-phase trials. The approach offers a practical, bias-controlled framework for integrating external data into regional drug development and regulatory decision making.

Multiple endpoints often arise in clinical trials where the relevant outcome, such as quality of life or clinical benefit, is defined by multiple outcomes and the interest is to test globally the overall difference between the two groups. It's increasingly needed to compare nonrandomized or imperfectly randomized groups in multiple endpoints for various disease areas, e.g., in neuro-protective, arthritis, and immunotherapies. For example, in our study on knee osteoarthritis, we wanted to compare an active treatment with an external control, one arm of a previous randomized trial on the same patient population, in multiple primary endpoints such as pain, stiffness, and functional scores to determine an overall benefit. In these cases, it is known that conventional estimators are biased when the treatment is not randomized. Robust causal estimates are then needed to correct the bias in nonrandomized studies while guarding against potential deviations from various assumptions. However, no such methods are available for multiple endpoints to our knowledge, partly due to the complexity of the estimand. We propose a robust causal semiparametric estimation method for multiple endpoints, and derive the asymptotic properties of the proposed estimator. Simulation studies are performed to evaluate the finite sample properties of the proposed method and compare it with the parametric and naive methods. We then apply the procedure to analyze the knee osteoarthritis clinical study.

BACKGROUND:Post-diagnosis smoking remains prevalent among head and neck squamous cell carcinoma (HNSCC) patients. Smoking cessation may improve patient outcomes. METHODS:A prospective longitudinal cohort study (2008-2014) included 835 newly diagnosed HNSCC patients and followed up for 7 years. Participants were categorized by smoking behavior (never smokers, former smokers, quitters, continuing smokers, and intermittent smokers). The primary outcomes were overall survival (OS) and recurrence-free survival (RFS). RESULTS:Smoking cessation after diagnosis was associated with significantly improved OS. Quitters had a 61% reduction in mortality risk compared to continuing smokers (HR: 0.39, 95% CI: 0.22, 0.69), with the greatest benefit in oral cavity cancer patients (HR: 0.28, 95% CI: 0.12, 0.65). Intermittent smokers also showed improved survival (HR: 0.50, 95% CI: 0.31, 0.79). RFS did not significantly differ based on smoking behavior. CONCLUSIONS:Smoking cessation post-diagnosis improves OS, particularly in oral cavity cancer patients, highlighting the importance of targeted smoking cessation interventions in HNSCC care.

B cell maturation antigen (BCMA) has emerged as a prominent immunotherapeutic target in multiple myeloma (MM) due to its restricted expression on MM cells, plasma cells and mature B cells, with minimal presence in other normal tissues. In this study, we demonstrate through RNA sequencing and flow cytometry analyses of acute myeloid leukemia (AML) cell lines and primary patient samples that BCMA is also a relevant AML-associated antigen. Its robust surface expression on AML cells positions it as a promising candidate for targeted immunotherapy. Functionally, our findings indicate that BCMA in AML operates similarly to its role in MM – engaging the NF-kB pathway upon ligand binding, thereby activating gene expression programs that support leukemia cell survival and proliferation. We assessed several BCMA-targeted immunotherapeutic strategies, including bispecific T-cell engagers (TCE) and chimeric antigen receptor (CAR) transduced T-cells, NK-cells, and macrophages. We found that TCE treatment and BCMA CAR engineering markedly improved effector cell mediated cytotoxicity against AML cells, underscoring BCMA’s potential as a viable therapeutic target in AML. Furthermore, BCMA- directed TCE therapy significantly augmented the anti-leukemic activity of adoptively transferred CD8+ T-cells in a human AML xenograft model. Taken together, these findings support BCMA as a novel immunotherapeutic target in AML. Leveraging existing BCMA-directed treatments developed for MM could enable rapid clinical translation and broaden immunotherapy options for patients with AML.

AbstractUnderrepresented populations’ participation in clinical trials remains limited, and the potential impact of genomic variants on drug metabolism remains elusive. This study aimed to assess the pharmacokinetics (PK) and pharmacogenomics (PGx) of ribociclib in self-identified Black women with hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2) advanced breast cancer. LEANORA (NCT04657679) was a prospective, observational, multicenter cohort study involving 14 Black women. PK and PGx were evaluated using tandem mass spectrometry and PharmacoScan™ microarray (including CYP3A5*3, *6, and *7). CYP3A5 phenotypes varied among participants: 7 poor metabolizers (PM), 6 intermediate metabolizers (IM), and one normal metabolizer (NM). The area under the curve did not significantly differ between PMs (39,230 h*ng/mL) and IM/NMs (43,546 h*ng/mL; p = 0.38). The incidence of adverse events (AEs) was also similar. We found no association between CYP3A5 genotype and ribociclib exposure. Continued efforts are needed to include diverse populations in clinical trials to ensure equitable treatment outcomes.

External data (e.g., real-world data (RWD) and historical data) have become more readily available. This has led to rapidly increasing interest in exploring and evaluating ways of utilizing external data to facilitate traditional clinical trials (TCT), especially for rare diseases with high unmet medical needs where a TCT would be impractical and/or unethical. In this article, we focus on hybrid studies that incorporate external data into randomized clinical trials to augment the control arm and explore a complex innovative design. A sequential adaptive design conducts multiple interim assessments to improve the accuracy of estimates of agreement between external data and current data. At each interim assessment, we apply the inverse probability weighted power prior (IPW-PP) method to adaptively borrow information from external data to account for confounding and heterogeneity. The randomization ratio is dynamically adjusted during the interim assessment based on accumulatively augmented information to reduce the sample size of the current trial. Additionally, the proposed design can be extended to allow interim analyses for early efficacy/futility stopping, that is, early assessment of trial success or failure based on accumulated data, potentially reducing ineffective treatment exposure and unnecessary time and resources. The performance of the proposed method and design is evaluated via extensive simulation studies. The sequential adaptive design and IPW-PP approach having desirable properties are implemented.

For observational studies or clinical trials not fully randomized, the baseline covariates are often not balanced between the treatment and control groups. In this case, the traditional estimates of treatment effects are biased, and causal inference method is needed to get unbiased estimate. The doubly robust estimator (DRE) is a recent popular development in casual inference. However, the unbiasedness of DRE relies on the correct specification of either propensity score or outcome models, which is hardly guaranteed. To overcome this issue, Yuan, Yin, and Tan (2021) proposed an enhanced doubly robust estimator which utilizes semiparametric models and nonparametric monotone link functions for both propensity score and outcome models. In this article, we further develop an enhanced doubly robust estimator with concave link functions for both propensity score and outcome models. The asymptotic properties of the enhanced doubly robust estimator are studied. Simulation studies are conducted to evaluate the proposed method. A clinical trial data analysis is used to illustrate the method.

With our increased ability to capture large data, causal inference has received renewed attention and is playing an ever-important role in biomedicine and economics. However, one major methodological hurdle is that existing methods rely on many unverifiable model assumptions. Thus robust modeling is a critically important approach complementary to sensitivity analysis, where it compares results under various model assumptions. The more robust a method is with respect to model assumptions, the more worthy it is. The doubly robust estimator (DRE) is a significant advance in this direction. However, in practice, many outcome measures are functionals of multiple distributions, and so are the associated estimands, which can only be estimated via U-statistics. Thus most existing DREs do not apply. This article proposes a broad class of highly robust U-statistic estimators (HREs), which use semiparametric specifications for both the propensity score and outcome models in constructing the U-statistic. Thus, the HRE is more robust than the existing DREs. We derive comprehensive asymptotic properties of the proposed estimators and perform extensive simulation studies to evaluate their finite sample performance and compare them with the corresponding parametric U-statistics and the naive estimators, which show significant advantages. Then we apply the method to analyze a clinical trial from the AIDS Clinical Trials Group.

We consider binary classification in the high-dimensional setting, where the number of features is huge, and the number of observations is limited. We focus on the setting where features in one group have certain correlation structures that are not present in the other group. This is particularly relevant in early detection of diseases where subjects develop from a normal or homeostatic state to a diseased condition. Linear discriminant analysis (with a link function) and classification based on regularized regression or machine learning have been used as methods for this problem and related variable selection. However, most methods do not account for the correlation structures of variables within groups. While the diseased group may demonstrate abundant diversity and no clear structure, achieving higher accuracy in classification requires considering the correlation structures in the control group with homeostasis. In this paper, we develop a structural equation modeling approach to characterize the correlation structures of homeostasis, and the parameters are estimated using only the data from one group. The structural equation models are not applicable to the data from the other group, and the classification specificity and sensitivity are determined by choosing the confidence intervals of the estimated parameters. We use a real multi-platform genomics dataset to illustrate the methods, and we demonstrate that our approach performs well compared to statistical learning methods such as regularized logistic regression models.

Inverse probability weighting (IPW) is frequently used to reduce or minimize the observed confounding in observational studies. IPW creates a pseudo-sample by weighting each individual by the inverse of the conditional probability of receiving the treatment level that he/she has actually received. In the pseudo-sample there is no variation among the multiple individuals generated by weighting the same individual in the original sample. This would reduce the variability of the data and therefore bias the variance estimate in the target population. Conventional variance estimation methods for IPW estimators generally ignore this underestimation and tend to produce biased estimates of variance. We here propose a more reasonable method that incorporates this source of variability by using parametric bootstrapping based on intra-stratum variability estimates. This approach firstly uses propensity score stratification and intra-stratum standard deviation to approximate the variability among multiple individuals generated based on a single individual whose propensity score falls within the corresponding stratum. The parametric bootstrapping is then used to incorporate the target variability by re-generating outcomes after adding a random error term to the original data. The performance of the proposed method is compared with three existing methods including the naïve model-based variance estimator, the nonparametric bootstrap variance estimator, and the robust variance estimator in the simulation section. An example of patients with sarcopenia is used to illustrate the implementation of the proposed approach. According to the results, the proposed approach has desirable statistical properties and can be easily implemented using the provided R code.

Precision nanomedicine can be employed as an alternative to chemo- or radiotherapy to overcome challenges associated with the often narrow therapeutic window of traditional treatment approaches, while safely inducing effective, targeted antitumor responses. Herein, we report the formulation of a therapeutic nanocomposite comprising a hyaluronic acid (HA)-coated gold nanoframework (AuNF) delivery system and encapsulated IT848, a small molecule with potent antilymphoma and -myeloma properties that targets the transcriptional activity of nuclear factor kappa B (NF-κB). The porous AuNFs fabricated via a liposome-templated approach were loaded with IT848 and surface-functionalized with HA to formulate the nanotherapeutics that were able to efficiently deliver the payload with high specificity to myeloma and lymphoma cell lines in vitro. In vivo studies characterized biodistribution, pharmacokinetics, and safety of HA-AuNFs, and we demonstrated superior efficacy of HA-AuNF-formulated IT848 vs free IT848 in lymphoma mouse models. Both in vitro and in vivo results affirm that the AuNF system can be adopted for targeted cancer therapy, improving the drug safety profile, and enhancing its efficacy with minimal dosing. HA-AuNF-formulated IT848 therefore has strong potential for clinical translation.

Background Estrogen and progesterone influence the immune system and thus may also influence clinical outcomes for patients (pts) with cancer receiving immune checkpoint inhibitor (ICI) therapy. Hence, we investigated if gender, premenopausal (PREMENO), and postmenopausal (POSTMENO) female states influenced ICI-associated clinical outcomes. Methods In our multicenter study based on real-world data, we identified pts receiving anti-PD-1 or anti-PD-L1 [PD(L)-1] ICI monotherapy between 1/2011 to 4/2018 with follow-up until 1/2021 using pharmacy records. Immune-related adverse events (irAEs) by CTCAE V4.03, and physician-assessed tumor responses and time to treatment failure (TTF) were collected. Women under the age of 55 years were considered PREMENO following the World Health Organization's suggested average menopausal age.1 We further investigated the non-small cell lung cancer (NSCLC) cohort receiving PD(L)-1 ICI in the metastatic setting for ICI efficacy analysis. Univariate analysis, multivariable logistic regression models, and Kaplan-Meier analyses were used to assess differences between male vs. PREMENO vs. POSTMENO. Results We identified 913 pts receiving PD(L)-1 ICI: 58% (n=528) nivolumab, 35% (320) pembrolizumab, 5% (47) atezolizumab, and 2% (18) others. The median age for the entire cohort (EC) was 68 years, 56% (514) were male, 36% (328) POSTMENO, 7% (67) PREMENO, 65% (591) White, and 21% (192) African American. The most common tumor types were NSCLC and melanoma in 46% (417) and 12% (109), respectively. Any grade and grade ≥3 irAEs were 32% (290) and 8% (76) for the EC. No difference among POSTMENO vs. PREMENO vs. male was noted for overall survival (OS) (p=0.2) or TTF (p=0.2). Similarly, no difference in any grade irAEs was noted among the study cohorts (p=0.24). Among 393 pts with NSCLC, 51% (202) were male, 44% (171) POSTMENO, and 5% (20) PREMENO. The NSCLC group consisted of 60% (239) White, 28% (109) African American, 15% (60) with a history of autoimmune disease, and 33% (128) with <2 sites of metastasis. Again, no difference in OS (p=0.6) or TTF (p=0.8) was observed among the three NSCLC study cohorts. Any grade irAEs were noted in 30% (115) and grade ≥3 irAEs in 7% of NSCLC patients (28). No difference in any grade irAEs was noted for the three NSCLC study cohorts (p=0.12). Conclusions In our study, gender, including female menopausal status, did not influence ICI safety or efficacy outcomes. While these findings are reassuring, further prospective studies, including pts with diverse cancer types and additional ICI regimens, are needed to universalize these findings. Ethics Approval This study was approved by the Georgetown University Medical Center Institutional Review Board; approval number MODCR00002093

Supplementary Data from Rapid Immune Recovery and Graft-versus-Host Disease–like Engraftment Syndrome following Adoptive Transfer of Costimulated Autologous T Cells