The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Richard Branson once wrote: “I have always valued capability over expertise. While you may need to hire specialists for some positions, take a close look at people who have thrived in different industries and jobs – they are usually more versatile, have transferable skills, and can potentially tackle problems creatively.”
He goes on to write: “Obviously a healthy mix of experience and novel thinking is the ideal, but on balance I would anticipate more fresh and objective solutions to flow from the smart and curious inexpert outsider than the ‘been there done that’ experts.”
Although the versatility to which Branson alludes is instrumental for successful statistical consulting, it is also obvious that an inexpert outsider would not be able to waltz into the drug development industry and make successful contributions. The expertise that is required is simply too much for a non-specialist. This raises an important question:
How should we, as an industry, walk this fine line between specialized expertise and versatile capabilities?
Consider, for example, the list of seven questions provided below.
When planning a conventional trial, one can anticipate the drug supply necessary for the trial by determining how the number of patients reflected in the sample size will distribute across the trial sites. Implementing an adaptive trial, by contrast, raises many challenges for predicting the necessary drug supply. It can require planning for different sample sizes depending on the outcome of an interim look; or preparing different dosages if certain arms of a multi-arm trial are to drop after the interim look. In the case of a biomarker-driven adaptive design, determining adequate drug supply may require the ability to predict which doses are necessary for different subpopulations at particular trial sites.
As more clinical trials make use of adaptive designs, investors have come to realize that high quality trial designs can result in significant improvements to a trial’s financial risk profile. Regardless of a trial’s eventual success or failure, a well-constructed design provides a drug with the highest possible probability of success while mitigating financial risk.
Cytel CTO Nitin Patel, recently sat down with ECHOES (a magazine for statistics in clinical trials) to discuss his vision of the future of statistics and statistical programming within the biopharmaceutical industry.
Patel argues that the global reach of statistics in an era of big data will affect both statistical technique and communication. As a result, he encourages aspiring statisticians to gain a solid foundation in advanced computational methods, Bayesian techniques, and also a range of global film and world literature.
As a seasoned entrepreneur, Patel also discusses what he views as three critical factors for successful entrepreneurship. Drawing on his experiences as a founder of Cytel, and also from his several years of teaching at the MIT Sloan School and the Indian Institute of Management, Patel notes the importance of developing risk-taking abilities, and drawing on trust and teamwork to counter some of the uncertainties of an unconventional career path.
A new JAMA study on discontinued randomized trials in Switzerland, Germany and Canada, reports that poor recruitment accounts for 101 out of 253 trials that were eventually discontinued (or about 10% of the 1017 trials which participated in the study). When restricted to industry-sponsored trials with non-healthy volunteers, poor recruitment accounted for the discontinuation of 40 trials out of 119 that were discontinued. Across the board, poor recruitment was the foremost cause of trial discontinuity.
The core methodological problem that would eventually spur the development of Cytel’s StatXact software was first posed by Harvard’s Marvin Zelen at a computational seminar in the late 1970s. Zelen, a distinguished professor of statistical sciences and head of the Department of Biostatistics at Harvard University, was also serving as the Director of the Dana Farber Cancer Institute.
The analysis of serious adverse events from cytotoxic agents in oncology trials were heavily dependent on an imprecise Cochran rule to measure the signifincance of small sample categorical data. The crude calculation meant that estimations of p-values were wide off the mark. Zelen challenged his students to find ways to expand Fisher’s exact test to r x c contingency tables, and by doing so to seal the promise of more effective development and delivery of urgent cancer treatments.
Cyrus Mehta and Nitin Patel took up Zelen’s challenge, publishing a series of papers on exact significance testing throughout the 1980s. Despite offering novel statistical solutions to persisting problems, the implementation of such solutions clearly required assistance from software. Unfortunately, few venture capitalists were willing to invest in a package of arcane statistical tests that were largely still in development.
Cytel was created with a grant from the National Cancer Institute, with a view to developing software that would make newer exact tests widely available for clinical studies. Its first software package, StatXact, is now used for exact testing in oncology, as well as environmental studies, public health, demography, law, and several areas of medicine and clinical development. The widespread use of exact tests has led to an array of intriguing research questions involving the power of various exact tests. Below we present a favorite finding, on the power of conditional versus unconditional exact tests: