COVID-19 and science advice on the 'Grand Stage': the metadata and linguistic choices in a scientific advisory groups' meeting minutes.

Science advice for governments attracted great scrutiny during the COVID-19 pandemic, with the public spotlight on institutions and individual experts-putting science advice on the 'Grand Stage'. A review of the academic literature identified transparency, a plurality of expertise, the science and policy 'boundary', and consensus whilst addressing uncertainty as key themes. The Scientific Advisory Group for Emergencies (SAGE) has been the primary provider of coordinated scientific and technical advice to the UK Government during emergencies since 2009. Using the first 89 of SAGE's meeting minutes (study period: 22 January 2020-13 May 2021), the 'metadata' and linguistic choices are analysed to identify how SAGE's role and protocols are communicated. This includes understanding which experts were regularly taking part in discussions, the role of scientific experts in the science advisory system and their influence on policy choices, and the degree of consensus and uncertainty within this group of experts-all of which relate to the degree of transparency with the public. In addition, a temporal analysis examines how these practices, such as linguistically marking uncertainty, developed over the period studied. Linguistic markers indexing certainty and uncertainty increased, demonstrating a commitment to precise and accurate communication of the science, including ambiguities and the unknown. However, self-references to SAGE decreased over the period studied. The study highlights how linguistic analysis can be a useful approach for developing an understanding of science communication practices and scientific ambiguity. By considering how SAGE presents to those outside the process, the research calls attention to what remains 'behind the scenes' and consequently limits the public's understanding of SAGE's role in the COVID-19 response.

A multistate model for early decision-making in oncology.

The development of oncology drugs progresses through multiple phases, where after each phase, a decision is made about whether to move a molecule forward. Early phase efficacy decisions are often made on the basis of single-arm studies based on a set of rules to define whether the tumor improves ("responds"), remains stable, or progresses (response evaluation criteria in solid tumors [RECIST]). These decision rules are implicitly assuming some form of surrogacy between tumor response and long-term endpoints like progression-free survival (PFS) or overall survival (OS). With the emergence of new therapies, for which the link between RECIST tumor response and long-term endpoints is either not accessible yet, or the link is weaker than with classical chemotherapies, tumor response-based rules may not be optimal. In this paper, we explore the use of a multistate model for decision-making based on single-arm early phase trials. The multistate model allows to account for more information than the simple RECIST response status, namely, the time to get to response, the duration of response, the PFS time, and time to death. We propose to base the decision on efficacy on the OS hazard ratio (HR) comparing historical control to data from the experimental treatment, with the latter predicted from a multistate model based on early phase data with limited survival follow-up. Using two case studies, we illustrate feasibility of the estimation of such an OS HR. We argue that, in the presence of limited follow-up and small sample size, and making realistic assumptions within the multistate model, the OS prediction is acceptable and may lead to better early decisions within the development of a drug.

Joint modeling of progression-free and overall survival and computation of correlation measures.

In this paper, we derive the joint distribution of progression-free and overall survival as a function of transition probabilities in a multistate model. No assumptions on copulae or latent event times are needed and the model is allowed to be non-Markov. From the joint distribution, statistics of interest can then be computed. As an example, we provide closed formulas and statistical inference for Pearson's correlation coefficient between progression-free and overall survival in a parametric framework. The example is inspired by recent approaches to quantify the dependence between progression-free survival, a common primary outcome in Phase 3 trials in oncology and overall survival. We complement these approaches by providing methods of statistical inference while at the same time working within a much more parsimonious modeling framework. Our approach is completely general and can be applied to other measures of dependence. We also discuss extensions to nonparametric inference. Our analytical results are illustrated using a large randomized clinical trial in breast cancer.