Trial design
Adapt to learn
Using data to modify the operating characteristics of an active trial in accordance with pre-specified rules, adaptive designs can make clinical trials more flexible, efficient and informative than fixed-sample designs. In oncology, where there’s often high variability between the way patients respond to treatment, this style of design can minimise patient exposure, as well as enable an early decision to terminate if the treatment appears to not be working. Mae Losasso speaks to Tianjian Zhou, assistant professor in the department of statistics at Colorado State University, and James Wason, professor of biostatistics at Newcastle University, to discuss the ins and outs of adaptive design.
his history of randomised controlled trials in medicine is relatively short: the first to be published was in 1948 by the British Medical Research Council (MRC). Four years earlier, the biologist Albert Schatz had isolated the bacteria that produced streptomycin, which proved effective against tuberculosis – then one of the highest causes of mortality among young adults in Europe and North America. The MRC established a trial to compare the use of streptomycin with the standard treatment for pulmonary tuberculosis – bed rest. Recruiting patients aged between 15 and 30, the trial gave both the streptomycin and control groups the same
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standard treatment and the results were undeniable: streptomycin was more efficacious than bed rest. Since then, this style of clinical trial has become a bedrock of the medical profession, for testing new drugs, new combinations of drugs and new drug courses. But in recent years, clinical trials have been witnessing the development of something new: adaptive designs. “Conventionally, a clinical trial is conducted with a given drug”, says Tianjian Zhou, assistant professor in the department of statistics at Colorado State University. “We [do] not add some other drug into the trial”, and we maintain “a fixed dosage of the drug”, as well as “a fixed sample size,
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