New business models for antibiotic innovation.

The increase in antibiotic resistance and the dearth of novel antibiotics have become a growing concern among policy-makers. A combination of financial, scientific, and regulatory challenges poses barriers to antibiotic innovation. However, each of these three challenges provides an opportunity to develop pathways for new business models to bring novel antibiotics to market. Pull-incentives that pay for the outputs of research and development (R&D) and push-incentives that pay for the inputs of R&D can be used to increase innovation for antibiotics. Financial incentives might be structured to promote delinkage of a company's return on investment from revenues of antibiotics. This delinkage strategy might not only increase innovation, but also reinforce rational use of antibiotics. Regulatory approval, however, should not and need not compromise safety and efficacy standards to bring antibiotics with novel mechanisms of action to market. Instead regulatory agencies could encourage development of companion diagnostics, test antibiotic combinations in parallel, and pool and make transparent clinical trial data to lower R&D costs. A tax on non-human use of antibiotics might also create a disincentive for non-therapeutic use of these drugs. Finally, the new business model for antibiotic innovation should apply the 3Rs strategy for encouraging collaborative approaches to R&D in innovating novel antibiotics: sharing resources, risks, and rewards.

Weighted and two-stage least squares estimation of semiparametric truncated regression models

This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of the coefficients in a truncated regression model. The distribution of the errors is unknown and permits general forms of unknown heteroskedasticity. Also provided is an instrumental variables based two-stage least squares estimator for this model, which can be used when some regressors are endogenous, mismeasured, or otherwise correlated with the errors. A simulation study indicates that the new estimators perform well in finite samples. Our limiting distribution theory includes a new asymptotic trimming result addressing the boundary bias in first-stage density estimation without knowledge of the support boundary. © 2007 Cambridge University Press.

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.