Propensity scores methodology is a popular approach to treatment effect estimation in observational research. Correctly specified propensity scores guarantee the balance of pre-treatment covariates ...between treatment and control groups, and lead to unbiased estimated treatment effect. The major challenge of propensity scores methodology is that the true propensity scores are unknown and need to be estimated using correctly specified model. Typically, researchers go through several rounds of manual specification of propensity scores model, followed by covariate balance check and model’s correction. In this dissertation a series of simulation studies were conducted to evaluate and compare Covariate Balancing Propensity Scores (CBPS) and entropy balancing methods, that automatically equate groups on pre-treatment measures, and do not require manual specification of propensity scores model or covariate balance check. The first part of this dissertation assessed balancing properties of these methods and compared with the traditional weighting techniques. According to the results, both CBPS and entropy balancing produced well-balanced data; CBPS showed slightly worse results when true propensity score model contained cubic effects. The second part of this work evaluated accuracy of treatment effect estimation (ATT) on the data that were preprocessed by these methods. When an outcome was continuous, all techniques including reference methods produced similar unbiased results. In the models with binary outcome CBPS and entropy balancing performed equally well with an exception of the scenarios with true propensity scores models containing cubic effects, when CBPS produced less biased results. In conditions with count outcome the difference between CBPS and entropy balancing was observed only in the scenarios of true propensity scores model with cubic effects, where entropy balancing produced less biased results.
Abstract Background The coronavirus disease 2019 (COVID-19) pandemic was characterized by rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, affecting viral ...transmissibility, virulence, and response to vaccines/therapeutics. EMPATHY (NCT04828161), a phase 2 study, investigated the safety/efficacy of ensovibep, a multispecific designed ankyrin repeat protein (DARPin) with multivariant in vitro activity, in ambulatory patients with mild to moderate COVID-19. Methods Nonhospitalized, symptomatic patients (N = 407) with COVID-19 were randomized to receive single-dose intravenous ensovibep (75, 225, or 600 mg) or placebo and followed until day 91. The primary endpoint was time-weighted change from baseline in log10 SARS-CoV-2 viral load through day 8. Secondary endpoints included proportion of patients with COVID-19–related hospitalizations, emergency room (ER) visits, and/or all-cause mortality to day 29; time to sustained clinical recovery to day 29; and safety to day 91. Results Ensovibep showed superiority versus placebo in reducing log10 SARS-CoV-2 viral load; treatment differences versus placebo in time-weighted change from baseline were −0.42 (P = .002), −0.33 (P = .014), and −0.59 (P < .001) for 75, 225, and 600 mg, respectively. Ensovibep-treated patients had fewer COVID-19–related hospitalizations, ER visits, and all-cause mortality (relative risk reduction: 78% 95% confidence interval, 16%–95%) and a shorter median time to sustained clinical recovery than placebo. Treatment-emergent adverse events occurred in 44.3% versus 54.0% of patients in the ensovibep and placebo arms; grade 3 events were consistent with COVID-19 morbidity. Two deaths were reported with placebo and none with ensovibep. Conclusions All 3 doses of ensovibep showed antiviral efficacy and clinical benefits versus placebo and an acceptable safety profile in nonhospitalized patients with COVID-19.
Abstract
Background
Ensovibep is a multi-specific DARPin (designed ankyrin repeat protein) antiviral in clinical development for treatment of COVID-19. In the Phase 2 EMPATHY study, ensovibep ...demonstrated greater viral load decline versus placebo. Here we report (1) the efficacy of ensovibep in patients with and without anti-SARS-CoV-2 antibodies at baseline and (2) SARS-CoV-2 mutation emergence data with treatment.
Methods
Eligible ambulatory patients with ≥2 COVID-19 symptoms (onset within 7 days) and positive SARS-CoV-2 rapid antigen test on day of dosing, were randomized (1:1:1:1) to ensovibep (600, 225 or 75 mg) or placebo as single, IV infusion. Chemiluminescent immunoassays were used for antibody detection (SARS-CoV-2 S1/S2 IgG and SARS-CoV-2 IgM). A pre-specified subgroup analysis was performed based on baseline anti-SARS-CoV-2 antibody status. Analysis of changes in viral genome from baseline to post baseline was performed to evaluate treatment-emergent mutations.
Results
Of the patients analyzed, 48.5% had anti-SARS-CoV-2 antibodies at baseline. Baseline log10 SARS-CoV-2 viral load (mean ±SD) was similar across groups ensovibep (all doses) 6.5 ±1.5, placebo 6.2 ±1.5; > 90% were infected with the Delta (B.1.617.2) variant. SARS-CoV-2 viral load reduction up to Day 8 showed similar effects in favor of ensovibep compared with placebo regardless of the presence of anti-SARS-CoV-2 antibodies (Figure 1). Patients in ensovibep 75 mg, 600 mg, and placebo groups had comparable incidences of emergent mutations, with a higher incidence in the 225 mg group. Based on analysis of 70% of the expected viral sequencing data, two mutations in the key binding residues of ensovibep were observed (Y489H and F486L) in a total of three patients treated with ensovibep. These patients either cleared virus by Day 8 or mutations were transient (occurred at a single time point but not later in the course of infection). Figure 1Forest plot of estimated treatment differences and associated 95% confidence intervals in time-weighted change from baseline in log10 SARS-CoV-2 viral load through Day 8 by subgroups for the presence of anti-SARS-CoV-2 antibodies (SARS-CoV-2 S1/S2 IgG and/or SARS-CoV-2 IgM) at baseline.
Conclusion
Ensovibep effectively reduces SARS-CoV-2 viral load regardless of the presence of anti-SARS-CoV-2 antibodies at baseline. Furthermore, there were no emerging mutations of concern, indicating that a single dose administration of ensovibep is associated with minimal selective pressure.
Disclosures
Marc Bonten, MD, PhD, Astra-Zeneca: Advisor/Consultant|Janssen: Advisor/Consultant|Merck: Advisor/Consultant|Novartis: Advisor/Consultant Richa Chandra, MD, Novartis Pharmaceuticals Corporation: Employee Damodaran Solai Elango, MD, Novartis Healthcare Pvt Ltd: Employee Pierre Fustier, PhD, Molecular Partners AG: Employee Kinfemichael Gedif, PhD, Novartis Pharmaceuticals Corporation: Employee Susana Goncalves, MD, Novartis Pharma AG: Employee Awawu Igbinadolor, MD, Novartis: Awawu Igbinadolor reports financial support from different pharmaceutical companies and organizations Jeff Kingsley, DO, MBA, CPI, FACRP, Centricity Research: Other Charles G. Knutson, PhD, Novartis Institutes for BioMedical Research: Employee Petra Kukkaro, PhD, Novartis Pharma AG: Employee Nagalingeswaran Kumarasamy, MD, Novartis: Nagalingeswaran Kumarasamy reports financial support from different pharmaceutical companies and organizations Philippe Legenne, MD, Molecular Partners AG: Employee Martha Mekebeb-Reuter, MD, Novartis: Martha Mekebeb-Reuter reports financial support from different pharmaceutical companies and organizations Krishnan Ramanathan, MD, Novartis Pharma AG: Employee Evgeniya Reshetnyak, PhD, Novartis Pharmaceuticals Corporation: Employee Michael Robinson, PhD, Novartis Institute for Tropical Disease: Employee Jennifer Rosa, MD, Novartis: Jennifer Rosa reports financial support from different pharmaceutical companies and organizations Marianne Soergel, MD, Molecular Partners AG: Employee Vaia Stavropoulou, PhD, Molecular Partners AG: Employee Nina Stojcheva, PhD, Molecular Partners AG: Employee Michael T. Stumpp, PhD, Molecular Partners AG: Employee Andreas Tietz, MD, Novartis Pharma AG: Employee Xiaojun Zhao, PhD, Novartis Institutes for BioMedical Research: Employee Zhaojie Zhang, PhD, 8. Novartis Institutes for BioMedical Research: Employee.
Heterogeneity of treatment effect in randomized clinical trials is referred to the problem of high variability in treatment responses across patients. Although a study may suggest positive average ...treatment effect for a trial population, for subgroups of individuals a treatment may be not beneficial. Despite the existence of a wide array of ad-hoc and post-hoc statistical procedures, which are designed to discern or control for such variations of responses, each of them has certain limitations. The purpose of the current study is to propose the statistical model of estimation of Heterogeneity of Treatment Effect (HTE) in randomized clinical trials using EM algorithm, the approach that so far has been overlooked in the clinical research literature.
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