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.
When conducting a clinical trial with small or sparse data sets, statistical methods meant for large sample sizes may fail to obtain an accurate interpretation of data. This is where computationally challenging exact methods often come into play.
Exact methods, however, are inferentially conservative in the sense that due to small sample sizes, the actual Type 1 error rate is often smaller than the nominal (intended) rate . There exists an array of strategies to combat this troublesome feature of exact tests, each of which varies along the parameter of computational complexity.