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Despite the importance of clinical trial data, there are problems with the way these data are presented. There is a need to simplify what is being discussed so that it can be more easily understood or summarized by patients and their advisers in their decision-making process.
Maurie Markman, MD
Data from clinical trials play a key role in decisions physicians routinely make in designing treatment plans for individual patients. This is certainly important in oncology, where such data help define the potential benefits versus the possible risks of therapeutic strategies. The relevance of the data is heightened by the fact that the benefits being discussed include the impact of a strategy on the duration of the individual’s survival, the time to progression of the disease process, and the impact on quality of life. Risks include short-term adverse events, such as emesis and hair loss; longer-term effects, such as neuropathy; and the potential for fatal events, such as neutropenic sepsis and therapy-associated heart failure.
Despite the importance of such information, there are problems with the way these data are presented that have not been adequately highlighted in the oncology literature. There is a need to simplify what is being discussed so that it can be more easily understood or summarized by patients and their advisers in their decision-making process.
This is a rather recent problem. In the not-so-distant past, discussions rarely took place because the existing options in most clinical settings were very limited and, unfortunately, in most situations, the only decision to be made was to either accept, or not accept, the recommendation of the treating physician. Today, the number of therapeutic options available and the medically reasonable variations of those approaches have expanded substantially in most circumstances. Further, patients frequently arrive at their initial visit or during subsequent follow-up visits with information regarding therapy generated from internet searches initiated by their own efforts or those of their family or friends.
Using Plain Language
As a result, there is a serious and ever-increasing risk for information overload in discussions between patients and their oncologists, particularly in light of the anxiety-provoking nature of the topics being reviewed. Therefore, open, honest, but also thoughtful and respectful discussion is required. Simplifying the data, without distorting or sugarcoating the message, has the realistic potential to aid in this complex and often difficult conversation.Consider, for a moment, a recent report from the medical literature about the evidence-based benefits of aspirin in preventing the subsequent development of cardiovascular disease (CVD) or colorectal cancer (CRC).1 Multiple studies conducted over many years have examined this highly clinically relevant issue. It would be easy to see how a conversation with a patient regarding the risks versus benefits of aspirin could be overwhelming. However, in this analysis, the authors summarized the complex literature and made a relatively simple recommendation to physicians to consider for use in their patients. They concluded:
“The decision to initiate low-dose aspirin use for the primary prevention of CVD and CRC in adults aged 60 to 69 years who have a 10% or greater 10-year CVD risk should be an individual one. Persons who are not at increased risk for bleeding, have a life expectancy of at least 10 years, and are willing to take low-dose aspirin daily for at least 10 years are more like to benefit.”1
In reviewing this recommendation, physicians and patients considering the benefits of this strategy need only consider a single percentage figure (10%) and period of time (10 years). How the physician and patient evaluate the data in an individual case will hopefully now become the focus of the subsequent conversation rather than a discussion of the difference between “10% versus 11.8%,” or a period of time of “10 years versus 7.5 years.”
Data can easily be translated into more simplified forms in the oncology sphere. Consider, as just 1 example, the relevant observation of the objective clinical activity and duration of survival associated with the administration of the check- point inhibitor pembrolizumab in a large patient population (n = 655) with advanced malignant melanoma.2 The report noted an overall objective response rate of 33% (194 of 581 patients) and 45% (60 of 133 patients) in individuals who did not receive prior therapy. Further, at the time of data cut-off for submitting this analysis, the response duration was at least 6 months or 1 year in 79% and 44% of patients, respectively. Median over- all survival was 23 months, with 12-month and 24-month survival rates of 66% and 49%, respectively. Among chemotherapy-naïve patients, the 12-month and 24-month survival rates were 73% and 60%, respectively.
How might these data be summarized if being presented to a patient considering treatment with this important antineoplastic agent?
One option would be to round off the percentage figures and present the data based on the number of patients who bene ted from treatment. So, the overall response rate could be described as “approximately 1 of 3 patients responded” and “1 of 2 patients” who had not received prior therapy, with the duration of response being at least 6 months or 12 months in “8 of 10” and “4 of 10,” respectively. Median overall survival could be “2 years in duration,” with 12-month and 24-month survival observed in “7 of 10” and “5 of 10” patients and in “7 of 10” and “6 of 10” patients who had received no prior chemotherapy.
The discussion of the summarized data also could be enhanced by simple visual presentations, such as in a pie chart or other such displays that could help an individual patient understand the relevance of the information being presented in his or her case.
This topic is worthy of serious additional discussion within the oncology community.
Maurie Markman, MD, editor-in-chief, is president of Medicine & Science at Cancer Treatment Centers of America, and clinical professor of Medicine, Drexel University College of Medicine.