PurposeSodium glucose co-transporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) have demonstrated cardioprotective effects in people with type 2 diabetes and atherosclerotic cardiovascular disease (ASCVD). In this patient group, there is treatment equipoise, from the standpoint of cardiovascular effect between these medication classes; however, factors associated with prescribing are poorly characterized.MethodsWe performed a retrospective real-world analysis by creating an electronic health record registry of people with type 2 diabetes and ASCVD (without additional indications for a specific cardioprotective class) who received a prescription for either an SGLT2i or GLP-1RA. We analyzed patient-, provider-, and clinical encounter-related predictors of being prescribed an SGLT2i or GLP-1RA using univariable and multivariable logistic regression analysis.ResultsA total of 573 eligible patients received either SGLT2i (N = 274) or GLP-1RA (N = 299) between January 2019 and October 2024. Care in cardiology (OR = 4.78; 95% CI, 2.53–9.04) strongly predicted SGLT2i prescription. Care in endocrinology (OR = 0.40; 95% CI, 0.23–0.68), higher BMI (OR = 0.92; 95% CI, 0.88–0.95, per BMI unit), and a higher recent estimated glomerular filtration (OR = 0.98; 95% CI, 0.96–0.99, per eGFR unit) predicted GLP-1RA prescription. The area under the receiver operating characteristic curve of the model was 0.78.ConclusionPrescriber's specialty strongly determined the selection of cardioprotective agents. Treatment guidelines should provide more specific guidance regarding patient selection and consider the holistic benefits of each drug class beyond their cardiovascular protective effects.

Introduction: In people with T2D and preexisting ASCVD, either SGLT2i or GLP1RA are indicated by treatment guidelines to reduce MACE. We evaluated predictors of prescription of SGLT2i vs GLP1RA in a population eligible for either. Methods: An electronic health record (EHR) based registry was created to identify people with T2D and ASCVD who were indicated either a GLP1RA or SGLT2i for cardiorenal protection within a large, academic health system. Data pertaining to demographics, lab and imaging results, ICD9/10 diagnoses, prescriptions, provider and clinic characteristics were extracted. Eligible encounters occurred in a primary care, endocrinology, cardiology, or nephrology clinic between January 1, 2019 and August 23, 2023. For each eligible encounter where a drug was prescribed, the first treatment type (GLP1RA or SGLT2i) was determined based on medication history. We estimated a logistic regression using stepwise variable selection to identify a best-predicting model and forced the variables of age, sex, and race into the model. Results: A total of 315 patients with T2D and ASCVD were eligible for either treatment and were prescribed one of these medications: 142 were prescribed a GLP1RA and 173 were prescribed SGLT2i. Lower BMI was associated with use of SGLT2i (OR = 0.91, 95% CI 0.87-0.96), as was being an established patient (OR 2.32, 95% CI 1.14-4.72). Compared to treatment in a primary care setting, treatment in a cardiology clinic was strongly associated with prescription of SGLT2i (OR = 7.77, 95% CI 3.18-19.04), whereas treatment in endocrinology clinic was strongly associated with prescription of a GLP1RA (OR = 0.35, 95% CI 0.18-0.68). Area under the receiver operating characteristic curve for the model was 0.82. Conclusion: In a real-world dataset from a large academic center, the selection of guideline directed therapy for patients with T2D and ASCVD was strongly determined by the provider’s specialty, highlighting an important opportunity for education. Disclosure S. Agarwal: None. M.A. Basit: None. M.E. Bowen: Research Support; Boehringer-Ingelheim. D. Heitjan: Consultant; Bluejay Diagnostics, Medcognetics, Sebela, Abbott, Macrogenics, Guardant, Bristol-Myers Squibb Company, Gilead Sciences, Inc. C. Mai: None. K. Marble: None. Z. Xiang: None. I. Lingvay: Consultant; Altimmune, Astra Zeneca, Bayer, Biomea, Boehringer-Ingelheim, Carmot, Cytoki Pharma, Eli Lilly, Intercept, Janssen/J&J, Mannkind, Mediflix, Merck, Metsera, Novo Nordisk, Pharmaventures, Pfizer, Sanofi. Research Support; NovoNordisk, Sanofi, Mylan, Boehringer-Ingelheim. Consultant; TERNS Pharma, The Comm Group, Valeritas, WebMD, and Zealand Pharma. Funding This study was supported by Boehringer Ingelheim Pharmaceuticals, Inc. (BIPI) and Lilly USA, LLC. The authors meet criteria for authorship as recommended by the International Committee of Medical Journal Editors (ICMJE) and were fully responsible for all aspects of the trial and publication development.

In clinical trials that are subject to noncompliance, the commonly used intention-to-treat estimand is valid as a causal effect of treatment assignment but is sensitive to the level of compliance. An alternative estimand, the complier average causal effect (CACE), measures the average effect of treatment received in the latent subset of subjects who would comply with either assigned treatment. Because the principal stratum of compliers can vary with the circumstances of the trial, CACE too depends on the compliance fraction. We propose a model in which an underlying latent proto-compliance interacts with trial characteristics to determine a subject's compliance behavior. When the latent compliance is independent of the individual treatment effect, the average causal effect is constant across compliance classes, and CACE is robust across trials and equal to the population average causal effect. We demonstrate the potential degree of sensitivity of CACE in a simulation study, an analysis of data from a trial of vitamin A supplementation in children, and a meta-analysis of trials of epidural analgesia in labor.

Supplemental Table 1. Biomarker Associations with Outcome

Supplemental Figure 1. Immunoblots of Patient Serum before and after Therapy Supplemental Figure 2. Distribution of 1/Nab Titers for the 40 Patients Supplemental Figure 3. Distribution of the Day 2 serum levels of Interferon-alpha Supplemental Figure 4. Lack of Correlation of lymphocyte or macrophage infiltration with survival. Supplemental Figure 5. Lack of Correlation of PDL1 staining with survival. Supplemental Figure 6. Correlation of the mRNA immunoscore with survival.

Supplemental Tables 1-6 Supplemental Table 1. Results of some recent trials of chemotherapy in MPM. Supplemental Table 2. Post-Trial Therapies Supplemental Table 3. Characteristics of Patients Selected for Flow Cytometry Analysis Supplemental Table 4. List of Immune Response Genes assayed by Nanostring Supplemental Table 5: Correlation of Antibody Response to MOS or Radiographic Response Supplemental Table 6. Flow Cytometry Results

Abstract The external validity of the estimated treatment effect from a clinical trial is in doubt when there are effect modifiers whose distribution in the target population differs from that in the trial. Adjusting an estimated treatment effect from a trial to predict its likely value for the target population is a process known as generalization. We review classical and contemporary approaches to this problem. The traditional method is post-stratification, or the reweighting of stratum-specific treatment effect estimates by population distribution proportions. Contemporary methods employ stratification or weighting techniques based on estimates of the probability that an individual is included in the trial, akin to the propensity score. These methods are more flexible in that they readily accommodate continuous effect modifiers. Estimating the probabilities, however, requires in principle that one have individual-level population data, which are seldom available for pharmaceutical trials. When the effect modifiers are all discrete, the post-stratification and probability-weighting approaches give essentially the same estimates. Naïvely computed standard errors with the contemporary methods may be inflated. We illustrate and compare generalization methods in a simulation and using data from the Lipids Research Clinics Coronary Primary Prevention Trial and the New York School Choice Experiment.

Legend for Supplementary Figures 1-2

We present a method to analyze sensitivity of frequentist inferences to potential nonignorability of the missingness mechanism. Rather than starting from the selection model, as is typical in such analyses, we assume that the missingness arises through unmeasured confounding. Our model permits the development of measures of sensitivity that are analogous to those for unmeasured confounding in observational studies. We define an index of sensitivity, denoted MinNI, to be the minimum degree of nonignorability needed to change the mean value of the estimate of interest by a designated amount. We apply our model to sensitivity analysis for a proportion, but the idea readily generalizes to more complex situations.

Supplementary Figure 1 from CCL2 Blockade Augments Cancer Immunotherapy

PDF file, 33KB, Data from the computer simulated model.

Abstract Altering the immunosuppressive microenvironment that exists within a tumor will likely be necessary for cancer vaccines to trigger an effective antitumor response. Monocyte chemoattractant proteins (such as CCL2) are produced by many tumors and have both direct and indirect immunoinhibitory effects. We hypothesized that CCL2 blockade would reduce immunosuppression and augment vaccine immunotherapy. Anti-murine CCL2/CCL12 monoclonal antibodies were administered in three immunotherapy models: one aimed at the human papillomavirus E7 antigen expressed by a non–small cell lung cancer (NSCLC) line, one targeted to mesothelin expressed by a mesothelioma cell line, and one using an adenovirus-expressing IFN-α to treat a nonimmunogenic NSCLC line. We evaluated the effect of the combination treatment on tumor growth and assessed the mechanism of these changes by evaluating cytotoxic T cells, immunosuppressive cells, and the tumor microenvironment. Administration of anti-CCL2/CCL12 antibodies along with the vaccines markedly augmented efficacy with enhanced reduction in tumor volume and cures of approximately half of the tumors. The combined treatment generated more total intratumoral CD8+ T cells that were more activated and more antitumor antigen–specific, as measured by tetramer evaluation. Another important potential mechanism was reduction in intratumoral T regulatory cells. CCL2 seems to be a key proximal cytokine mediating immunosuppression in tumors. Its blockade augments CD8+ T-cell immune response to tumors elicited by vaccines via multifactorial mechanisms. These observations suggest that combining CCL2 neutralization with vaccines should be considered in future immunotherapy trials. Cancer Res; 70(1); 109–18

Supplemental Figure 1. Number of cycles received

Additional Trial related information and methods

Supplemental Figure 2. Representative biomarker staining

The ISNI (index of sensitivity to local nonignorability) method quantifies local sensitivity of parametric inferences to nonignorable missingness in an outcome variable. Here we extend ISNI to the situations where both outcomes and predictors can be missing and where the missingness mechanism can be either parametric or semi-parametric. We define the quantity MinNI (minimum nonignorability) to be an approximation to the norm of the smallest value of the transformed nonignorability that gives a nonnegligible displacement of the estimate of the parameter of interest. We illustrate our method in a complete data set from which we synthetically delete observations according to various patterns. We then apply the method to real-data examples involving the normal linear model and conditional logistic regression.

The COVID-19 pandemic arrived very suddenly and presented an immediate challenge to the clinical trials community. How should clinical research into treatments and prevention strategies be organized and conducted for a fast-moving, global pandemic? It is only relatively recently that West Africa was hit by the deadly Ebola epidemic, and our journal devoted an entire issue in 2016 to lessons learned from that experience. However, COVID-19 is different in many important ways that have implications for the research process. It is a true global pandemic, vastly greater in scope than Ebola. Its clinical course is much less deadly in relative terms but vastly more deadly in absolute terms. These differences offer much greater opportunity to conduct meaningful, definitive clinical trials during the course of the pandemic that have the potential to be consequential. As a result, literally thousands of clinical trials have already been registered on clinicaltrials.gov as of early June 2020. In this issue, we feature three articles that address methodologic aspects of the design of clinical trials on COVID-19. In the first of these, Susan Ellenberg provides an overview of the response to the pandemic. She draws on the history of clinical trials during notable previous epidemics such as human immunodeficiency virus, severe acute respiratory syndrome, H1N1 and Ebola, cataloguing some of the problems faced in these settings. One of the most striking aspects about COVID-19 is the huge research response, in part because of the scope of COVID-19, the fact that it has hit economically developed countries with strong existing infrastructure for clinical research, and because there are lots of available candidate treatments. There is no question that the pace of research and the speed with which clinical trials have been initiated is nothing short of breathtaking. Nevertheless, there has to date been precious little evidence of success, and although the outpouring of research effort has been remarkable, one senses a general absence of coordination. In the second article, a multi-national team of experts focus on a critical technical issue – what are the appropriate endpoints for clinical trials of COVID-19? The authors argue that the breadth of the clinical effects of COVID-19 is too large to focus on a single endpoint that would suit all clinical trials, and that the primary endpoint has to be titrated to the severity of disease in the population under investigation. They catalogue a host of clinically important outcomes and potential surrogate markers. The authors argue that death is not a suitable endpoint due to low statistical power. This has been fiercely debated in recent social media, and we do not expect this to be the last word on whether death should be the gold standard endpoint in these trials, given that COVID-19 has claimed more than 400,000 lives in just 3 months. The authors nonetheless explain that the amelioration of serious adverse symptoms of COVID-19 is clinically valuable in its own right. In a detailed simulation, they contrast the power of a recommended ‘time to recovery’ endpoint with an alternative fixed-time endpoint based on the World Health Organization recommended ordinal scale, demonstrating that time to recovery has only modest losses of power compared with a fixed endpoint evaluated at the optimal time point, while having the advantage of being easy to interpret. In the third article, Steven Piantadosi approaches study design from a more global, strategic perspective. He starts with the axiom that the ‘traditional’ randomized clinical trial is unsuited to the task and challenges us to contemplate how things could be done better. The key question here is the following. What is ‘special’ about COVID-19 that makes it different from the numerous other health conditions that we seek to address, and how should these differences alter the appropriate design of the global research effort? He characterizes the unique features of COVID-19 as