1.1. Introduction
In the past 50 years, the value of medicine has been clearly demonstrated by a longer life expectancy, a lower infant mortality rate, and a higher quality of life many of our senior citizens have been enjoying. Since the introduction of stomach-acid-blocking H2 antagonist drugs in the late 70's, the number of surgeries to treat ulcer has been greatly reduced. Childhood vaccination has literally wiped out diphtheria, whooping cough, measles, and polio in the U.S. Deaths from heart disease have been cut by more than half since 1950 and continue to decline. Even though we still face great challenges in combating cancer, great strides have been made in treating childhood leukemia. Early detection has led to successful treatment of some types of cancer such as the breast cancer. Treatments for schizophrenia and bipolar disorder have allowed many patients to live almost normal lives. A report(2006) on the value of medicine can be found at the Pharmaceutical Research and Manufacturers of America (PhRMA) website.
The use of statistics to support discovery and testing of new medicines has grown rapidly since the Kefauver-Harris Amendments which became effective in 1962. The Kefauver-Harris Amendments required drug sponsors to prove a product's safety and efficacy in controlled clinical trials in order to market the product. Since the Amendments, the number of statisticians working in the pharmaceutical industry has greatly increased. This increase took another jump when the manufacturing process came under close scrutiny. As we move into the 21st century, the lure and the promise of genomics and proteomics will further intensify scientists' reliance on statistics. The need to enhance our overall knowledge about diseases, the need to insert more points into the decision-making process, and the need to bring economics into development strategy considerations will undoubtedly present new opportunities for statisticians.
Even in the face of new opportunities, there are many well-established roles for statisticians in the pharmaceutical industry. The word "well-established" is a relative term since new roles will become more established over time. For example, trial simulation and modeling, viewed as new advancements a decade ago, has now become a common practice to help design better trials across the pharmaceutical industry.
Concerned that the current medical product development path may have become increasingly challenging, inefficient, and costly, the U.S. Food and Drug Administration (FDA)issued a document in March 2004 entitled "Challenge and Opportunity on the Critical Pathto New Medical Products". The document attements to bridge the technological disconnect between discovery and the product development process. The disconnect is thought to be largely due to the fact that the pace of development work has not kept up with the rapid advances in product discovery. The document addresses three major scientific and technical dimensions in the critical path of product development. The three dimensions relate to safety assessment, demonstration of a product's medical utility (benefit or effectiveness), and the product's industrialization (scaling up). In addition to understanding thechallenges, establishing the right standards and developing better toolkits for each dimension will be key to our ultimate success in overcoming the perceived stagnation in getting new drugs and biologics to the market. Statisticians, with their training in quantification and logical, thinking, can play a major role in the preparation and the execution of the action plan.
The call for innovation is nothing new for the pharmaceutical industry. The industry, as a whole, has made great strides in its basic science research in recent years. Cutting edge techniques are being developed on a daily basis to probe into the biologic origin and genetic connection of diseases. The research of microarrays and genomics has produced more data than could be perceived just a few years ago. With the race to unlock the mysteries of many diseases and finding cures for them, statistical support needs to be broadened in dimensions and increased in depth. Time has never been more right for statisticians to work alongside with their colleagues, being discovery scientists, clinicalpersonnel, manufacturing engineers, or regulatory colleagues. The collaboration should not only help transform data to knowledge, but also use knowledge for better risk-based decisions.
In this chapter, we will briefly cover some traditional statistical support to show how statistics has been used in many aspects of drug development. Our coverage is by no means exhaustive. It is simply an attempt to illustrate how broad statistical applications have been. We will also highlight some areas where a statistician's contribution will be crucial in moving forward, in view of the FDA's Critical Path initiative and the pharmaceutical industry's collective effort to take advantage of the FDA's call for innovation.