The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
By Ashwini Joshi
For small sample data or rare events data, exact non-parametric tests perform better than asymptotic tests. But they come with the disadvantage of conservativeness. Many corrections have been suggested to reduce this conservativeness but none of them solve the problems entirely. StatXact provides various methods of computing exact p-values. Depending upon the problem at hand, the user can decide which one to use.
Let’s consider a hypothetical example of stratified count data. The example shows two sample data with two strata. Events in Treatment1 are rare as compared to the ones in Treatment0. But the event rates are comparable.
Why do we do what we do? At Cytel we have always been driven to deliver benefits in the service of human health, and ultimately to bring new drugs to the patients who need them. In the context of our work in statistical software, we have recently had the opportunity to support an important conservation project defined by a similar passion to make a difference.
While our core focus has always been in biostatistics, and thus in the life science industry, our StatXact statistical software is used by customers across a broad spectrum of natural and social sciences thanks to its ability to handle small sample sizes.
The Association Takh is one such customer. Takh is dedicated to the re-introduction of the world’s last wild horse, as well as conservation in Mongolia and improvement of the lives of Mongolian herders. (1) In 2004 and 2005 the association reintroduced 22 Przewalski’s horses( one of the world’s most endangered species) from Le Villaret in southern France to the buffer zone of Khar Us Nuur National Park in the Khomyn Tal herder community of western Mongolia.
For the second installment of our StatXact 25th Anniversary Retrospective Series, Professor Joan Hilton (UC San Francisco) reflects on her pioneering work on exact conditional inferences.
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: