An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

Background Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily included any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.

The effect of COVID-19 vaccination in Italy and perspectives for living with the virus

COVID-19 vaccination is allowing a progressive release of restrictions worldwide. Using a mathematical model, we assess the impact of vaccination in Italy since December 27, 2020 and evaluate prospects for societal reopening after emergence of the Delta variant. We estimate that by June 30, 2021, COVID-19 vaccination allowed the resumption of about half of pre-pandemic social contacts. In absence of vaccination, the same number of cases is obtained by resuming only about one third of pre-pandemic contacts, with about 12,100 (95% CI: 6,600-21,000) extra deaths (+27%; 95% CI: 15–47%). Vaccination offset the effect of the Delta variant in summer 2021. The future epidemic trend is surrounded by substantial uncertainty. Should a pediatric vaccine (for ages 5 and older) be licensed and a coverage >90% be achieved in all age classes, a return to pre-pandemic society could be envisioned. Increasing vaccination coverage will allow further reopening even in absence of a pediatric vaccine.

The implications of uncertain renewable resource potentials for global wind and solar electricity projections

Studies exploring long-term energy system transitions rely on resource cost-supply curves derived from estimates of renewable energy (RE) potentials to generate wind and solar power projections. However, estimates of RE potentials are characterized by large uncertainties stemming from methodological assumptions that vary across studies, including factors such as the suitability of land and the performance and configuration of technology. Based on a synthesis of modeling approaches and parameter values used in prior studies, we explore the implications of these uncertain assumptions for onshore wind and solar PV electricity generation projections globally using the Global Change Analysis Model (GCAM). We show that variability in parametric assumptions related to land use (e.g., land suitability) are responsible for the most substantial uncertainty in both wind and solar generation projections. Additionally, assumptions about the average turbine installation density and turbine technology are responsible for substantial uncertainty in wind generation projections. Under scenarios that account for climate impacts on wind and solar energy, we find that these parametric uncertainties are far more significant than those emerging from differences in climate models and scenarios in a global assessment, but uncertainty surrounding climate impacts (across models and scenarios) have significant effects regionally, especially for wind. Our analysis suggests the need for studies focusing on long-term energy system transitions to account for this uncertainty.

Ambiguity Aversion and Mutual Funds: Evidence from China

I provide empirical evidence of ambiguity averse investors’ behaviour in Chinas mutual funds market. My analysis is motivated by the substantial uncertainty in China’s mutual funds market, and theoretical research of decision indicates that investors would be more ambiguity averse when face higher uncertainty. The most substantial implication of the empirical research is that investors tend to place more weight on the worst signal. Across multiple horizons, fund flows will also display more sensitivity to the worst performance. I also conduct robustness test about the different rank funds by Morningstar rating and compare the positive and negative performance during the minimum performance period.

Statistical emulation of a perturbed basal melt ensemble of an ice sheet model to better quantify Antarctic sea level rise uncertainties

Antarctic ice shelves are vulnerable to warming ocean temperatures, and some have already begun thinning in response to increased basal melt rates. Sea level is therefore expected to rise due to Antarctic contributions, but uncertainties in its amount and timing remain largely unquantified. In particular, there is substantial uncertainty in future basal melt rates arising from multi-model differences in thermal forcing and how melt rates depend on that thermal forcing. To facilitate uncertainty quantification in sea level rise projections, we build, validate, and demonstrate projections from a computationally efficient statistical emulator of a high-resolution (4 km) Antarctic ice sheet model, the Community Ice Sheet Model version 2.1. The emulator is trained to a large (500-member) ensemble of 200-year-long 4 km resolution transient ice sheet simulations, whereby regional basal melt rates are perturbed by idealized (yet physically informed) trajectories. The main advantage of our emulation approach is that by sampling a wide range of possible basal melt trajectories, the emulator can be used to (1) produce probabilistic sea level rise projections over much larger Monte Carlo ensembles than are possible by direct numerical simulation alone, thereby providing better statistical characterization of uncertainties, and (2) predict the simulated ice sheet response under differing assumptions about basal melt characteristics as new oceanographic studies are published, without having to run additional numerical ice sheet simulations. As a proof of concept, we propagate uncertainties about future basal melt rate trajectories, derived from regional ocean models, to generate probabilistic sea level rise estimates for 100 and 200 years into the future.

Truth and Uncertainty in Political Justification

Political deliberation and decision-making typically take place in circumstances of substantial uncertainty about what should be done. Some of this uncertainty concerns decision-relevant empirical facts and some of it concerns decision-relevant normative facts. It is widely accepted that uncertainty about empirical facts should make us cautious and that political justification must take such uncertainty into account. Some have argued, however, that uncertainty about empirical and normative facts is not symmetrical, and that normative uncertainty does not demand the same caution. This chapter argues that the argument against symmetry does not work in the political context and that political justification must take normative uncertainty into account.

Modeling Robustness of COVID-19 Containment Policies

We report on results from the application of a new computational model designed to address challenges faced by policymakers in designing and implementing COVID-19 containment measures in the face of substantial uncertainty and heterogeneity.

Variability of Black Carbon mass concentration in surface snow at Svalbard

Black Carbon (BC) is a significant forcing agent in the Arctic, but substantial uncertainty remains to quantify its climate effects due to the complexity of the different mechanisms involved, in particular related to processes in the snow-pack after deposition. In this study, we provide detailed and unique information on the evolution and variability of BC content in the upper surface snow layer during the spring period in Svalbard (Ny-Ålesund). Two different snow-sampling strategies were adopted during spring 2014 and 2015, providing the refractory BC (rBC) mass concentration variability on a seasonal/daily and daily/hourly time scales. The present work aims to identify which atmospheric variables could interact and modify the mass concentration of BC in the upper snowpack, the snow layer which BC particles affects the snow albedo. Despite the low BC mass concentrations, a relatively high daily variability was observed. Atmospheric, meteorological, and snow-related physico-chemical parameters were considered in a multiple statistical model to separate the factors determining observations. Precipitation events were the main drivers of the BC variability. Snow metamorphism and activation of local sources during the snow melting periods appeared to play a non-negligible role (wind resuspension in specific Arctic areas where coal mines were present). The BC content in the snow resulted in being statistically decoupled from the atmospheric BC load.

Ethical and Psychosocial Implications of Genomic Newborn Screening

The potential for genomic screening of the newborn, specifically adding genomic screening to current newborn screening (NBS), raises very significant ethical issues. Regardless of whether NBS of this type would include entire genomes or only the coding region of the genome (exome screening) or even sequencing specific genes, the ethical issues raised would be enormous. These issues include the limitations of bioinformatic interpretation of identified variants in terms of pathogenicity and accurate prognosis, the potential for substantial uncertainty about appropriate diagnosis, therapy, and follow-up, the possibility of much anxiety among providers and parents, the potential for unnecessary treatment and “medicalizing” normal children, the possibility of adding large medical costs for otherwise unnecessary follow-up and testing, the potential for negatively impacting medical and life insurance, and the almost impossible task of obtaining truly-informed consent. Moreover, the potentially-negative consequences of adding genomic sequencing to NBS might jeopardize all of NBS which has been and continues to be so beneficial for thousands of children and their families throughout the world.

Incorporating Multi-Year Temperature Predictions for Water Resources Planning

Multi-year climate predictions provide climate outlooks years to a decade in advance. As multi-year temperature predictions become more mainstream and skillful, guidance is needed to assist practitioners who wish to explore this maturing field. This paper demonstrates the process and considerations of incorporating multi-year temperature predictions into water resources planning. Multi-year temperature predictions from the Community Earth System Model Decadal Prediction Large Ensemble are presented as discrete and probabilistic products, and used to force two common hydrologic modeling approaches, conceptual and empirical. The approaches are demonstrated to simulate streamflow in the Upper Colorado River Basin watershed in Colorado, US, where diagnostics show that increasing temperatures are associated with decreasing streamflows. Using temperature information for lead years 2-6, two analyses are performed: (i) a retrospective hindcast for the climatological period (1981-2010), and (ii) a blind forecast for 2011-2015. For the retrospective hindcast, including temperature information improved the percent error as compared to climatology. For the blind forecast, the multi-year temperature prediction for warming was skillful, but the corresponding multi-year average streamflow predictions from both approaches were counterintuitive: with the predicted warming, the multi-year average streamflow was predicted to be lower than the climatological mean, however the observed multi-year average streamflow was higher than the climatological mean. This was due to above average precipitation during the prediction time frame, particularly one of the years. Removing that year, the multi-year streamflow average became lower than the climatological mean. Temperature provides a marginal source of streamflow predictability, but there will be substantial uncertainty until prediction skill for year-to-year climate variability, especially for precipitation, increases.