A hybrid Bayesian-frequentist approach to evaluate clinical trial designs for tests of superiority and non-inferiority

Seventy-five years of impactful environmental and occupational health research at the Nelson Institute of Environmental Medicine at New York University

Founded in 1947 as the Institute of Industrial Medicine, the Nelson Institute and Department of Environmental Medicine at New York University (NYU) Grossman School of Medicine (NYUGSOM) was supported by a National Institute of Environmental Health Science (NIEHS) Center Grant for over 56 years. Nelson Institute researchers generated 75 years of impactful research in environmental and occupational health, radiation effects, toxicology, and cancer. Environmental health research is continuing at NYUGSOM in its departments of medicine and population health. The objective of this historical commentary is to highlight the major achievements of the Nelson Institute and the department in the context of its history at facilities in Sterling Forest, Tuxedo, NY and Manhattan, NY. Aspects of our discussion include leadership, physical facilities, and research in many areas, including air pollution, health effects of environmental radiation exposures, inhalation toxicology methodology, carcinogenesis by chemicals, metals, and hormones, cancer chemoprevention, human microbiome, ecotoxicology, epidemiology, biostatistics, and community health concerns. The research of the institute and department benefited from unique facilities, strong leadership focused on team-based science, and outstanding investigators, students, and staff. A major lasting contribution has been the training of hundreds of graduate students and postdoctoral fellows, many of whom have been and are training the next generation of environmental and occupational health researchers at various institutions.

A Review of Bayesian Perspectives on Sample Size Derivation for Confirmatory Trials

Sample size derivation is a crucial element of planning any confirmatory trial. The required sample size is typically derived based on constraints on the maximal acceptable Type I error rate and minimal desired power. Power depends on the unknown true effect and tends to be calculated either for the smallest relevant effect or a likely point alternative. The former might be problematic if the minimal relevant effect is close to the null, thus requiring an excessively large sample size, while the latter is dubious since it does not account for the a priori uncertainty about the likely alternative effect. A Bayesian perspective on sample size derivation for a frequentist trial can reconcile arguments about the relative a priori plausibility of alternative effects with ideas based on the relevance of effect sizes. Many suggestions as to how such "hybrid" approaches could be implemented in practice have been put forward. However, key quantities are often defined in subtly different ways in the literature. Starting from the traditional entirely frequentist approach to sample size derivation, we derive consistent definitions for the most commonly used hybrid quantities and highlight connections, before discussing and demonstrating their use in sample size derivation for clinical trials.

Conditional equivalence testing: An alternative remedy for publication bias

We introduce a publication policy that incorporates "conditional equivalence testing" (CET), a two-stage testing scheme in which standard NHST is followed conditionally by testing for equivalence. The idea of CET is carefully considered as it has the potential to address recent concerns about reproducibility and the limited publication of null results. In this paper we detail the implementation of CET, investigate similarities with a Bayesian testing scheme, and outline the basis for how a scientific journal could proceed to reduce publication bias while remaining relevant.