A review and validation of overall survival extrapolation in health technology assessments of cancer immunotherapy by the National Institute for Health and Care Excellence: how did the initial best estimate compare to trial data subsequently made available?

Background: Validation of overall survival (OS) extrapolations of immune-checkpoint inhibitors (ICIs) during the National Institute for Health and Care Excellence (NICE) Single Technology Assessment (STA) process is limited due to data still maturing at the time of submission. Inaccurate extrapolation may lead to inappropriate decision-making. The availability of more mature trial data facilitates a retrospective analysis of the plausibility and validity of initial extrapolations. This study compares these extrapolations to subsequently available longer-term data.

Methods: A systematic search of completed NICE appraisals of ICIs from March 2000 to December 2017 was performed. A targeted search was also undertaken to procure published OS data from the pivotal clinical trials for each identified STA made available post-submission to NICE. Initial Kaplan-Meier curves and associated extrapolations from NICE documentation were extracted to compare the accuracy of OS projections versus the most mature data.

Results: The review identified 11 STAs, of which 10 provided OS data upon submission to NICE. The extrapolations undertaken considered parametric or piecewise survival models. Additional data cut-offs provided a mean of 18 months of OS beyond the end of the original data. Initial extrapolations typically under-estimated OS from the most mature data cut-off by 0.4–2.7%, depending on the choice of assessment method and use of the manufacturer- or ERG-preferred extrapolation.

Conclusion: Long-term extrapolation of OS is required for NICE STAs based on initial immature OS data. The results of this study demonstrate that the initial OS extrapolations employed by manufacturers and ERGs generally predicted OS reasonably well when compared to more mature data (when available), although on average they appeared to underestimate OS. This review and validation shows that, while the choice of OS extrapolation is uncertain, the methods adopted are generally aligned with later-published follow-up data and appear appropriate for informing HTA decisions.     

Price and Volatility Transmission and Market Power in the German Fresh Pork Supply Chain

We investigate the relationship between the transmission of price volatility and market power in the German fresh pork supply chain. We use a theoretical model underpinning this relationship followed by an empirical application that uses monthly farm, slaughterhouse and retail pork price data for the period 2000–2011. We examine both the relationships of market power with price level transmission and price volatility transmission in the chain. We use a vector error correction model and least squares regressions to analyse price transmission and price volatility transmissions, respectively. Results show that retail market power limited both types of transmissions. Competition inducing policy measures coupled with measures that support price risk management initiatives of chain actors are suggested.     

The cost of diabetes and obesity in Australia

Aims: To assess and compare the direct healthcare and non-healthcare costs and government subsidies by body weight and diabetes status.

Methods: The Australian Diabetes, Obesity and Lifestyle study collected health service utilization and health-related expenditure data at the 2011–2012 follow-up surveys. Costing data were available for 4,409 participants. Unit costs for 2016–2017 were used where available or were otherwise inflated to 2016–2017 dollars. Age- and sex-adjusted costs per person were estimated using generalized linear models.

Results: The annual total direct cost ranged from $1,998 per person with normal weight to $2,501 per person with obesity in participants without diabetes. For those with diabetes, total direct costs were $2,353 per person with normal weight, $3,263 per person with overweight, and $3,131 per person with obesity. Additional expenditure as government subsidies ranged from $5,649 per person with normal weight and no diabetes to $8,085 per person with overweight and diabetes. In general, direct costs and government subsidies were higher for overweight and obesity compared to normal weight, regardless of diabetes status, but were more noticeable in the diabetes sub-group. The annual total excess cost compared with normal weight people without diabetes was 26% for obesity alone and 46% for those with obesity and diabetes.

Limitations: Participants included in this study represented a healthier cohort than the Australian population. The relatively small sample of people with both obesity and diabetes prevented a more detailed analysis by obesity class.

Conclusion: Overweight and obesity are associated with increased costs, which are further increased in individuals who also have diabetes. Interventions to prevent overweight and obesity or reduce weight in people who are overweight or obese, and prevent diabetes, should reduce the financial burden.     

Consumer Regulation Strategies: Attenuating the Effect of Consumer References in a Voting Context

Consumption cues (e.g., brands, money, and advertisements) can have powerful effects on cognition, perception, and behavior, yet how people regulate responses to such cues is not well understood. This is surprising given that consumption cues are increasingly present in nontraditional consumer contexts, such as healthcare, education, and politics. This research develops a measure of two types of consumer regulation strategies, cue‐based and budget‐based (studies 1–4), and demonstrates that these strategies influence how people respond to consumption cues in a political context (study 5). Specifically, in a study involving the 2012 American Presidential Election, priming survey participants as consumers (versus citizens) influenced both voting intentions and self‐reported voting behavior, and the newly developed consumer regulation scale was instrumental in detecting this effect. These findings suggest there may be merit in the escalating debate and concern over referring to voters as consumers.     

On the forecasting of the challenging world future scenarios

Logistic and power law methodologies for both retrospective and prospective analyses of extended time series describing evolutionary growth processes, in environments with finite resources, are confronted. While power laws may eventually apply only to the early stages of said growth process, the Allee logistic model seems applicable over the entire span of a long range process. On applying the Allee logistic model to both the world population and the world gross domestic product time series, from 1 to 2008 AD, a projection was obtained that along the next few decades the world should experience a new economic boom phase with the world GDP peaking around the year 2020 and proceeding from then on towards a saturation value of about 142 trillion international dollars, while the world population should reach 8.9 billion people by 2050. These results were then used to forecast the behavior of the supply and consumption of energy and food, two of the main commodities that drive the world system. Our findings suggest that unless the currently prevailing focus on economic growth is changed into that of sustainable prosperity, human society may run into a period of serious economical and social struggles with unpredictable political consequences.     

A new methodology for the logistic analysis of evolutionary S-shaped processes: Application to historical time series and forecasting

A new multi-logistic methodology to analyze long range time series of evolutionary S-shaped processes is presented. It conceptually innovates over the traditional logistic approach. The ansatz includes computing the residuals to an optimized multi-logistic trend curve least squares fitted to the time-series data. The elements of the residuals series are checked for autocorrelations and once detected the residuals series is further analyzed to search for eventual presence of underlying periodic structures using a truncated Fourier sine series. The method foundations ensures both a universal applicability and a capacity to disclose the existence of active clocks that can be possibly traced to the driving motors of the evolutionary character of the time series, due to the responsiveness of corresponding process to the development of economic cycles. On associating these two views, it is found that the methodology has a strong potential to improve the quality of short-term forecasts. These findings have been put to test through applications of the methodology to studying the time evolution of two commodities of strong economic and social importance (corn and steel) and good results were consistently obtained for both the analytical and forecasting aspects.     

Full versus quasi MLE for ARMA-GARCH models with infinitely divisible innovations

We compare the backtesting performance of ARMA-GARCH models with the most common types of infinitely divisible innovations, fit with both full maximum likelihood estimation (MLE) and quasi maximum likelihood estimation (QMLE). The innovation types considered are the Gaussian, Student’s t, α-stable, classical tempered stable (CTS), normal tempered stable (NTS) and generalized hyperbolic (GH) distributions. In calm periods of decreasing volatility, MLE and QMLE produce near identical performance in forecasting value-at-risk (VaR) and conditional value-at-risk (CVaR). In more volatile periods, QMLE can actually produce superior performance for CTS, NTS and α-stable innovations. While the t-ARMA-GARCH model has the fewest number of VaR violations, rejections by the Kupeic and Berkowitz tests suggest excessively large forecasted losses. The α-stable, CTS and NTS innovations compare favourably, with the latter two also allowing for option pricing under a single market model.     

Labor Market Analysis and Public Policy: The Case of Morocco

This article uses detailed industry and household data to understandwhy Morocco's labor market performed poorly in 1985–95.The data indicate that marked structural changes and weak demandin the product market were responsible. This article makes twocontributions to the literature. The first is specific: it underscoresthat the demand for labor is a derived demand and that the performanceof the product market is an important determinant of the performanceof the labor market. The second is more general: it demonstratesthat this kind of microeconomic analysis, using data sets thatare often available in developing countries, can inform policydesign.     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.