Collette, Laurence

Background There is currently a lack of consensus on how health-related quality of life and other patient-reported outcome measures in cancer randomized clinical trials are analyzed and interpreted. This makes it difficult to compare results across randomized controlled trials (RCTs) synthesize scientific research, and use that evidence to inform product labeling, clinical guidelines, and health policy. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data for Cancer Clinical Trials (SISAQOL) Consortium aims to develop guidelines and recommendations to standardize analyses of patient-reported outcome data in cancer RCTs. Methods and Results Members from the SISAQOL Consortium met in January 2017 to discuss relevant issues. Data from systematic reviews of the current state of published research in patient-reported outcomes in cancer RCTs indicated a lack of clear reporting of research hypothesis and analytic strategies, and inconsistency in definitions of terms, including “missing data,”“health-related quality of life,” and “patient-reported outcome.” Based on the meeting proceedings, the Consortium will focus on three key priorities in the coming year: developing a taxonomy of research objectives, identifying appropriate statistical methods to analyze patient-reported outcome data, and determining best practices to evaluate and deal with missing data. Conclusion The quality of the Consortium guidelines and recommendations are informed and enhanced by the broad Consortium membership which includes regulators, patients, clinicians, and academics.

Current staging and risk-stratification methods in oncology, while helpful, fail to adequately predict malignancy aggressiveness and/or response to specific treatment. Increased knowledge of cancer biology is generating promising marker candidates for more accurate diagnosis, prognosis assessment, and therapeutic targeting. To apply these exciting results to maximize patient benefit, a disciplined application of well-designed clinical trials for assessing the utility of markers should be used. In this article, we first review the major issues to consider when designing a clinical trial assessing the usefulness of a predictive marker. We then present two classes of clinical trial designs: the Marker by Treatment Interaction Design and the Marker-Based Strategy Design. In the first design, we assume that the marker splits the population into groups in which the efficacy of a particular treatment will differ. This design can be viewed as a classical randomized clinical trial with upfront stratification for the marker. In the second design, after the marker status is known, each patient is randomly assigned either to have therapy determined by their marker status or to receive therapy independent of marker status. The predictive value of the marker is assessed by comparing the outcome of all patients in the marker-based arm to that of all of the patients in the non–marker-based arm. We present detailed sample size calculations for a specific clinical scenario. We discuss the advantages and disadvantages of the two trial designs and their appropriateness to specific clinical situations to assist investigators seeking to design rigorous, marker-based clinical trials.