Developing a preference-based utility scoring algorithm for the Psoriasis Area Severity Index (PASI)

Abstract

Introduction: It is challenging to identify health state utilities associated with psoriasis because generic preference-based measures may not capture the impact of dermatological symptoms. The Psoriasis Area Severity Index (PASI) is one of the most commonly used psoriasis rating scales in clinical trials. The purpose of this study was to develop a utility scoring algorithm for the PASI.

Methods: Forty health states were developed based on PASI scores of 40 clinical trial patients. Health states were valued in time trade-off interviews with UK general population participants. Regression models were conducted to crosswalk from PASI scores to utilities (e.g. OLS linear, random effects, mean, robust, spline, quadratic).

Results: A total of 245 participants completed utility interviews (51.4% female; mean age?=?45.3?years). Models predicting utility based on the four PASI location scores (head, upper limbs, trunk, lower limbs) had better fit/accuracy (e.g. R², mean absolute error [MAE]) than models using the PASI total score. Head/upper limb scores were more strongly associated with utility than trunk/lower limb. The recommended model is the OLS linear model based on the four PASI location scores (R²?=?0.13; MAE?=?0.03). An alternative is recommended for situations when it is necessary to estimate utility based on the PASI total score.

Conclusions: The derived scoring algorithm may be used to estimate utilities based on PASI scores of any treatment group with psoriasis. Because the PASI is commonly used in psoriasis clinical trials, this scoring algorithm greatly expands options for quantifying treatment outcomes in cost-effectiveness analyses of psoriasis therapies. Results indicate that psoriasis of the head/upper limbs could be more important than trunk/lower limbs, suggesting reconsideration of the standard PASI scoring approach.     

Does slack always affect resilience? A study of quasi-medium-sized Italian firms

Research on organizational slack, which has focused mainly on its effect in large, publicly traded firms and on transitional economies, has found that slack functions as a buffer in periods of crisis. However, little work has been done on the value of slack resources for smaller firms in mature industries. This study contributes to the resource-based literature with a quantitative analysis of a broad sample of Italian SMEs that operate in the traditional ‘Made in Italy’ industries. The purpose of the paper is to use longitudinal data from before and after the 2008 world financial crisis to determine whether slack resources drive growth and profitability in organizations with limited resources that operate in mature industries in periods of recession. The results of two-stage least squares regression indicate that, similar to their larger counterparts, small firms must secure high levels of profitability in order to achieve sound growth during recessions. Potential financial slack is equally important in driving profitability in these periods, although it is not related to higher growth. Investing in R&D does not affect small firms’ ability to be profitable and grow during recessions.     

Productivity growth and stock returns: firm- and aggregate-level analyses

A firm’s stock return is affected not only by its own productivity growth rate, but also by other firms’ productivity growth rates. We show that this spillover effect is significant and time-varying, and underlies a fallacy of composition observed in late 20th century U.S. data: stock returns and productivity growth are correlated positively in firm-level data but negatively in aggregate data. This seeming fallacy of composition reflects Schumpeterian creative destruction: a few technology winners’ stocks rise with their rising productivity while many technology losers’ stocks fall with their declining productivity. Thus, most individual firms’ stock returns correlate negatively with aggregate productivity growth. This implies that technological innovation need not be a blessing for all firms and as a result, for investors holding the market. Our findings also provide a firm-level technology innovation-based explanation of prior findings that the market return correlates negatively with aggregate earnings.     

Introduction to monetary and macro economics

This introduces the symposium on monetary and macro economics.     

How do firms make money selling digital goods online?

We review research on revenue models used by online firms who offer digital goods. Such goods are non-rival, have near zero marginal cost of production and distribution, low marginal cost of consumer search, and low transaction costs. Additionally, firms can easily observe and measure consumer behavior. We start by asking what consumers can offer in exchange for digital goods. We suggest that consumers can offer their money, personal information, or time. Firms, in turn, can generate revenue by selling digital content, brokering consumer information, or showing advertising. We discuss the firm’s trade-off in choosing between the different revenue streams, such as offering paid content or free content while relying on advertising revenues. We then turn to specific challenges firms face when choosing a revenue model based on either content, information, or advertising. Additionally, we discuss nascent revenue models that combine different revenue streams such as crowdfunding (content and information) or blogs (information and advertising). We conclude with a discussion of opportunities for future research including implications for firms’ revenue models from the increasing importance of the mobile Internet.     

Experience and Learning with Improved Technologies: Evidence from Improved Biomass Cookstoves in Ethiopia

Environmental and Resource Economics - This paper provides field experiment-based evidence that user experience and learning can increase the attractiveness of an important new cooking technology...     

Non-buoyant puff and plume dispersion modeling issues

This workshop was organized as a discussion group for a panel of dispersion modeling experts aimed at identifying and resolving (to whatever extent possible) the major debatable issues involved in the appropriate application and interpretation of dense gas dispersion models to real and potential scenarios.