Christopher P. Adams

Spending on New Drug Development

This paper replicates DiMasi et al. (J. Health Econ. 2003; 22: 151-185; Drug Inf. J. 2004; 38: 211-223) estimates of expenditure on new drug development using publicly available data. The paper estimates that average expenditure on drugs in human clinical trials is around

Pharmaceutical Development Phases: A Duration Analysis

27m per year, with

The Sealed-Bid Abstraction in Online Auctions

17m per year on drugs in Phase I,

Optimal Team Incentives with CES Production

34m on drugs in Phase II and

New Drug Development: Estimating entry from human clinical trials

27m per year on drugs in Phase III of the human clinical trials. The papers estimated expenditure on new drug development is somewhat greater than suggested by the survey results presented in DiMasi et al. (J. Health Econ. 2003; 22: 151-185; Drug Inf. J. 2004; 38: 211-223). The paper combines a 12-year panel of research and development expenditure for 183 publicly traded firms in the pharmaceutical industry with panel of drugs in human clinical trials for each firm over the same period. The paper estimates drug expenditure by estimating the relationship between research and development expenditure and the number of drugs in development for 1682 company/years (183 firms multiplied by the number of years for which we have financial and drug development information). The paper also estimates expenditure on drugs in various therapeutic categories.

Finite Mixture Models with One Exclusion Restriction

This paper estimates a duration model of late stage drug development in the pharmaceutical industry using publicly available data. The paper presents descriptive results on the estimated relationship between a particular drugs characteristics such as therapy category, route of administration and originators size, and that drugs pathway through the three stages of human clinical trials and regulatory review. The results suggest that drugs with longer durations are less likely to succeed, drugs from larger firms are more likely to succeed and faster in the later phases of development, and that durations fell between 1995 and 2002.

Agent Discretion, Adverse Selection, and the Risk-Incentive Trade-Off

This paper presents five empirical tests of the popular modeling abstraction that assumes bids from online auctions with proxy bidding can be analyzed “as if” they were bids from a second-price sealed-bid auction. The tests rely on observations of the magnitudes and timings of the top two proxy bids, with the different tests stemming from different regularity assumptions about the underlying distribution of valuation signals. We apply the tests to data from three eBay markets---MP3 players, DVDs, and used cars---and we reject the sealed-bid abstraction in all three data sets. A closer examination of these rejections suggests that they are driven by less experienced bidders. This consistent rejection casts doubt on several existing theories of online auction behavior and suggests some demand estimates based on the abstraction can be biased. To assess the direction and magnitude of this bias, we propose and estimate a new model in which some bidders conform to the abstraction while other bidders bid in a reactive fashion. Because reactive bidding can be at least partially detected from the data, we are able to estimate the underlying distribution of demand and compare it to what the sealed-bid abstraction implies. We find that our proposed model fits the data better, and our demand estimates reveal a large potential downward bias were we to assume the second-price sealed-bid model instead.

Identifying Demand in Ebay Auctions

Standard economic theory and intuition suggests that the free rider problem will overwhelm firm-wide incentives in large firms. Despite this, such schemes are relatively common in manufacturing firms and may be more popular in larger firms. This paper models an optimal incentive contract and a simple partnership with CES production functions. The results suggest that when employee inputs are complements, firm-wide incentives such as profit sharing and partnerships are quite valuable, even in large firms.

Learning in Prediction Markets

This paper analyses a detailed data set on drugs in human clinical trials around the world between 1989 and 2002. The data provides information on the probabilities with which drugs successfully complete the different phases of the trials and the durations of successful completions. The paper shows that success rates and durations can vary substantially across observable characteristics of the drugs, including primary indication, originating company, route of administration and chemistry. It suggests that analysis of this type of data can help us to answer questions such as: Do AIDS drugs get to market faster? Do Biotech drugs have higher probabilities of getting to market? This paper provides some general statistics for analyzing these questions.

Stochastic Matrix Factorization

This paper characterizes the identified set for finite mixture models with one exclusion restriction on the data‐generating process. It provides necessary and sufficient conditions on the observed data for point identification and for when the identified set has measure zero. The results are illustrated in a simulation study and with data from a randomized controlled trial on chemotherapy for colon cancer as well as with data from an observational study used to estimate returns to schooling.