This is the first of a three part post in which we will consider (i) improvements to trial quality that result from bundling data management with biostatistics, (ii) reductions in cost and study length that result from bundling data management with biostatistics, and (iii) the contributions of statistical innovation to clinical data management, such as those by Cytel Board member Professor Marvin Zelen (Research Professor and former Chair of Biostatistics at the Harvard School of Public Health.)
May 22, 2014 4:28:05 PM
May 8, 2014 7:54:00 AM
( Editor's note: This post has been refreshed in December 2016)
Model based algorithms for Phase I dose-escalation have been in existence for nearly thirty years. Despite guarantees of increased statistical power and greater accuracy, there remains a clear preference for rule based algorithms amongst clinicians. The explanation for this is as old as the models themselves.
May 5, 2014 10:21:00 PM
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.
Apr 21, 2014 3:57:00 PM
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: