Topics:

ODAC Subcommittee Assessing Problems in Quality-of-Life Studies of Cancer Drugs

ODAC Subcommittee Assessing Problems in Quality-of-Life Studies of Cancer Drugs

BETHESDA, Md—At the request of the Food and Drug Administration, a special subcommittee of the Oncologic Drugs Advisory Committee (ODAC) has begun assessing issues related to the use of quality-of-life (QOL) endpoints in the approval process for new oncology drugs.

“We would like quality-of-life endpoints to be hypothesis driven, and discussed prospectively in designing the protocol,” said Richard Pazdur, MD, director of the FDA’s Division of Oncology Drug Products.

At its first meeting, the subcommittee explored three key topics—the definition of QOL measures, the clinical significance of interpretations of QOL data, and issues relating to QOL data analysis.

The 11-member subcommittee, three of whom are ODAC members, is chaired by David Cella, PhD, director of the Center on Outcomes Research and Education, Northwestern University Medical School. “It is the goal of this subcommittee to make clear and concrete recommendations that are as specific as is reasonable,” Dr. Cella said.

In 1985, ODAC took the position that QOL endpoints could serve as the basis for approving new cancer medications. FDA has interpreted that view to mean that “for drugs that do not have an impact on survival, demonstration of a favorable effect on quality of life would be considered more compelling than improvements in other measures, such as objective tumor response rate.”

In seeking updated guidance from ODAC, FDA noted that pharmaceutical companies have increasingly incorporated quality-of-life endpoints into randomized, controlled trials. Yet most oncology drugs continue to be approved based on traditional efficacy endpoints, such as survival or tumor response.

Indeed, ODAC members themselves often criticize the “quality” of QOL data submitted in support of a drug and seldom seem to regard them as important in deciding whether to recommend approval.

“An issue I find particularly troubling is that it is relatively uncommon that the quality-of-life data presented as part of the drug application are actually hypothesis driven,” said Richard L. Schilsky, MD, ODAC chair and associate dean for clinical research, University of Chicago Medical Center.

Trial design is often based on the usual efficacy endpoints of survival and time to progression, and those endpoints then drive the sample size for the study, he said. “Frequently, what happens then is that there are descriptive quality-of-life analyses added in, often without any hypothesis being proposed by the investigator as to what quality-of-life changes might be expected to occur,” he said. “As a result, the quality-of-life analysis is grossly underpowered because the sample size is really not adequate.”

On the issue of defining QOL, the subcommittee reached consensus on three general points:

  • Patients are the experts in determining the status of their quality of life, and their input is needed in formulating the instruments used to assess quality of life.

  • No single measure is now capable of assessing quality of life, nor will one emerge.

  • Changes in symptoms are important but not sufficient in themselves for assessing quality of life as the basis for approving a cancer drug.

Four major issues influence the defining of quality of life, said Carol Moinpour, PhD, of the Division of Public Health Sciences of the Southwest Oncology Group (SWOG): (1) Whether quality of life is an objective construct in which the patient’s perception is critical, (2) whether quality of life is just health-related or involves a broader construct, (3) how broad must quality-of-life impacts be and which domains are relevant, and (4) to what extent are psychological or social theories important in explaining the impact of therapy on quality of life.

“In general, in cancer clinical trials, we have talked about quality of life as really measuring health status,” Dr. Moinpour said.

Umbrella Term

Donald Patrick, PhD, professor of public health and community medicine, University of Washington, said that part of the problem “is the use of this umbrella term of quality of life. It is often used as a synonym for a patient’s self-report.”

He called patient perceptions “the bedrock of quality-of-life research in the last 5 to 10 years,” but posed the question of which patient perceptions are individual, which are universal, and how do the two influence an accurate assessment of quality of life.

Finally, Jody L. Pelusi, PhD, cancer program coordinator, Maryvale Hospital, Phoenix, interjected another factor—the role cultural differences may play in affecting the concept of quality of life for different people. “This may become an issue as we recruit more minorities and ethnic groups into clinical trials,” she said.

In turning to the question of the clinical significance of QOL data, Jeff A. Sloan, PhD, called it “probably the most unnerving question” about QOL measurements. “If you ask, how are you doing, we can always say how we’re doing,” said Dr. Sloan, lead statistician at the Mayo Clinic’s cancer center. “But how do you quantify that?”

Clearly, clinical significance does not equate with statistical significance, he added. “Just because you have a P value of less than 0.05 does not mean you have a clinically significant outcome.”

Three Basic Approaches

Today, researchers use three basic approaches to assess changes in quality of life. One is searching for meaningful changes in group comparisons, which is usually done by looking at differences between means or medians. “That’s probably the gold standard. Whether it’s an acceptable gold standard is open to discussion right now,” Dr. Sloan said.

Another approach is to look at significant shifts in category scores on an individual item, such as depression. A third approach involves statistically adjusting group-comparison data to provide insight into the individual items measured.

“We’ve been looking at clinical significance, bringing together all of the literature, and what is both interesting and puzzling, but very satisfying and almost comforting, is that all of the methods tend to say the same thing,” Dr. Sloan said. If a set of scores changes by half a standard deviation, “people will say we have something—independent of sample size, independent of the tool or the measure being looked at.”

Placebo Effect

Stacy R. Nerenstone, MD, urged obtaining more information about the importance of the placebo effect and investigator bias in influencing patients’ perceptions of their quality of life.

“Most people would agree that symptoms can be influenced by taking a pill or being involved in a trial,” said Dr. Nerenstone, an oncologist at the Helen & Harry Gray Cancer Center, Hartford, Connecticut. “Investigator bias is extraordinarily difficult to quantify.”

She called understanding these influences “very important when you are going to have drugs that potentially could be licensed because of their effectiveness on symptom control.”

Dr. Schilsky called for greater interaction between clinical researchers and those carrying out QOL studies. “It is critically important that as trials are designed, the two investigators get together and think carefully about what are the clinically important parameters that should be measured and how to best measure them,” he said.

Several subcommittee members questioned whether cancer studies whose primary endpoints are such traditional measures as survival or response duration can reliably assess quality of life, in part because their sample sizes may be too small.

Dr. Pazdur said that one approach under consideration is to conduct two studies, one focused on traditional clinical endpoints and the other on QOL endpoints. “This option is starting to evolve in our discussions with the companies. Many quality-of-life tools are added on without serious discussion with clinical investigators,” he said.

Analyzing data from QOL studies is difficult because the issue is multidimensional and measurements are done over time, said Diane Fairclough, DrPH, an invited presenter at the meeting. One important issue is how much missing data can be allowed.

“Unfortunately, there is no magic rule,” said Dr. Fairclough, of the AMC Cancer Research Center, Lakewood, Colorado. “What would be acceptable in an adjuvant breast cancer study would be very different from what would be acceptable in a pancreatic cancer study because of the real difference in the mortality and morbidity associated with these cancers.”

However, Nan Laird, MD, chair of biostatistics, Harvard School of Public Health, said the literature on QOL studies suggests that “5% or less of missing data is not a bad rule.”

Good QOL analysis begins with good planning of a trial. “I can’t give you a nice, simple formula for handling an analysis of quality of life,” Dr. Fairclough said. “But I can say this. Careful planning in the design phase is critical.”

 
Loading comments...

By clicking Accept, you agree to become a member of the UBM Medica Community.