SEATTLE, WA—A new technique developed by statisticians helps doctors optimize dosing new cancer treatments in phase I/II clinical trials. The technique was presented at the 2015 Joint Statistical Meetings (JSM).
When a promising new experimental anticancer treatment is developed, the only way to determine how it affects humans is to use it to treat actual cancer patients. To establish an optimal dose, a phase I/II clinical trial is conducted, during which a sequence of small cohorts of two to three patients are given varying doses of the experimental treatment.
When the clinical outcomes of each cohort are observed, their data are added to the accumulated dose-outcome data from all previous patients and this data is used to choose the best dose for the next cohort. When the phase I/II trial is completed, the final best dose is selected to treat future patients.
Although the notion of dose finding assumes that a single dose is administered to each patient, this is not always the case in reality.
“Medical treatment often involves multiple cycles of therapy. Physicians routinely choose a patient’s treatment in each cycle adaptively based on the patient’s history of treatments and clinical outcomes,” explained Juhee Lee, PhD, assistant professor of applied mathematics and statistics at the University of California Santa Cruz, in the presentation. As such, a patient’s therapy is not a single treatment but rather a sequence of treatments, each chosen using an adaptive algorithm of the general form “observe, treat; observe, treat; and so forth.”
Most clinical trial designs do not account for the multistage treatment regimens used by the physicians who treat patients during the trial. Instead, conventional trial designs consider only the initial treatments, as if each patient’s outcomes are due to the first cycle of treatment, and disregard treatments in the second cycle.
In a dose-finding trial, each new patient’s first dose, cycle 1, is chosen using adaptive rules based on results that were observed in earlier trial patients. In conventional designs, the rules disregard the patient’s cycle 1 dose and outcomes when choosing the patient’s cycle 2 dose. As a result, the physician must choose each patient’s cycle 2 dose informally, based on his or her intuition. Unfortunately, when making treatment decisions in multiple stages, using intuition can lead to bad decision-making by even highly experienced physicians.
The Optimal Two-Cycle Dose-Finding Design was motivated by this problem, which is experienced frequently in early phase clinical trials of potential new anticancer agents. Phase I/II trials establish each new patient’s dose based on good outcomes, called treatment efficacy, such as tumor shrinkage as well as negative outcomes such as toxicity.
This new dose-finding design is the first to deal with the problem of optimizing each patient’s dose levels in two cycles in phase I/II cancer clinical trials. Extensive computer simulations have shown the two-cycle design often is 30% to 35% better than conventional methods in terms of how well it performs in choosing the best dose levels for patients.