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Background/Aims: Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without a priori specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population. Methods: We propose a permutation procedure for tuning parameter selection that allows for type I error control when testing for heterogeneous treatment effects framed within the Virtual Twins procedure for subgroup identification. We verify that the type I error rate can be controlled at the nominal rate and investigate the power for detecting heterogeneous effects when present through extensive simulation studies. We apply our method to a secondary analysis of data from a randomized trial of very low nicotine content cigarettes. Results: In the absence of type I error control, the observed type I error rate for Virtual Twins was between 99% and 100%. In contrast, models tuned via the proposed permutation were able to control the type I error rate and detect heterogeneous effects when present. An application of our approach to a recently completed trial of very low nicotine content cigarettes identified several variables with potentially heterogeneous treatment effects. Conclusions: The proposed permutation procedure allows researchers to engage in secondary analyses of clinical trials for treatment effect heterogeneity while maintaining the type I error rate without pre-specifying subgroups. [ABSTRACT FROM AUTHOR]

Background: Ensovibep (MP0420) is a designed ankyrin repeat protein, a novel class of engineered proteins, under investigation as a treatment of SARS-CoV-2 infection.Objective: To investigate if ensovibep, in addition to remdesivir and other standard care, improves clinical outcomes among patients hospitalized with COVID-19 compared with standard care alone.Design: Double-blind, randomized, placebo-controlled, clinical trial. (ClinicalTrials.gov: NCT04501978).Setting: Multinational, multicenter trial.Participants: Adults hospitalized with COVID-19.Intervention: Intravenous ensovibep, 600 mg, or placebo.Measurements: Ensovibep was assessed for early futility on the basis of pulmonary ordinal scores at day 5. The primary outcome was time to sustained recovery through day 90, defined as 14 consecutive days at home or place of usual residence after hospital discharge. A composite safety outcome that included death, serious adverse events, end-organ disease, and serious infections was assessed through day 90.Results: An independent data and safety monitoring board recommended that enrollment be halted for early futility after 485 patients were randomly assigned and received an infusion of ensovibep (n = 247) or placebo (n = 238). The odds ratio (OR) for a more favorable pulmonary outcome in the ensovibep (vs. placebo) group at day 5 was 0.93 (95% CI, 0.67 to 1.30; P = 0.68; OR > 1 would favor ensovibep). The 90-day cumulative incidence of sustained recovery was 82% for ensovibep and 80% for placebo (subhazard ratio [sHR], 1.06 [CI, 0.88 to 1.28]; sHR > 1 would favor ensovibep). The primary composite safety outcome at day 90 occurred in 78 ensovibep participants (32%) and 70 placebo participants (29%) (HR, 1.07 [CI, 0.77 to 1.47]; HR < 1 would favor ensovibep).Limitation: The trial was prematurely stopped because of futility, limiting power for the primary outcome.Conclusion: Compared with placebo, ensovibep did not improve clinical outcomes for hospitalized participants with COVID-19 receiving standard care, including remdesivir; no safety concerns were identified.Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

Chen et al. (2022) recently proposed a set of estimating equations that incorporate data from secondary endpoints to improve precision in parameter estimates related to a primary endpoint. We were motivated to translate their methodology to the context of randomized controlled trials to gain precision in treatment effect estimation using data from secondary endpoints. Our results suggest that this estimator cannot gain efficiency in this context because of random treatment assignment, especially when there is a treatment effect on secondary endpoints, and that further methodological work in this area is needed. [ABSTRACT FROM AUTHOR]