Demystifying the estimand framework: a case study using patient-reported outcomes in oncology

Patient-reported outcomes (PROs), such as symptoms, function, and other health-related quality-of-life aspects, are increasingly evaluated in cancer randomised controlled trials (RCTs) to provide information about treatment risks, benefits, and tolerability. However, expert opinion and critical review of the literature showed no consensus on optimal methods of PRO analysis in cancer RCTs, hindering interpretation of results. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data Consortium was formed to establish PRO analysis recommendations. Four issues were prioritised: developing a taxonomy of research objectives that can be matched with appropriate statistical methods, identifying appropriate statistical methods for PRO analysis, standardising statistical terminology related to missing data, and determining appropriate ways to manage missing data. This Policy Review presents recommendations for PRO analysis developed through critical literature reviews and a structured collaborative process with diverse international stakeholders, which provides a foundation for endorsement; ongoing developments of these recommendations are also discussed.

Patient-reported outcomes are increasingly used in health research, including randomized controlled trials and observational studies. However, the validity of results in longitudinal studies can crucially hinge on the handling of missing data. This paper considers the issues of missing data at each stage of research. Practical strategies for minimizing missingness through careful study design and conduct are given. Statistical approaches that are commonly used, but should be avoided, are discussed, including how these methods can yield biased and misleading results. Methods that are valid for data which are missing at random are outlined, including maximum likelihood methods, multiple imputation and extensions to generalized estimating equations: weighted generalized estimating equations, generalized estimating equations with multiple imputation, and doubly robust generalized estimating equations. Finally, we discuss the importance of sensitivity analyses, including the role of missing not at random models, such as pattern mixture, selection, and shared parameter models. We demonstrate many of these concepts with data from a randomized controlled clinical trial on renal cancer patients, and show that the results are dependent on missingness assumptions and the statistical approach. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

The American Society of Clinical Oncology (ASCO) convened a subcommittee to develop recommendations on improving the evidence base for treating older adults with cancer in response to a critical need identified by the Institute of Medicine. Older adults experience the majority of cancer diagnoses and deaths and make up the majority of cancer survivors. Older adults are also the fastest growing segment of the US population. However, the evidence base for treating this population is sparse, because older adults are underrepresented in clinical trials, and trials designed specifically for older adults are rare. The result is that clinicians have less evidence on how to treat older adults, who represent the majority of patients with cancer. Clinicians and patients are forced to extrapolate from trials conducted in younger, healthier populations when developing treatment plans. This has created a dearth of knowledge regarding the risk of toxicity in the average older patient and about key end points of importance to older adults. ASCO makes five recommendations to improve evidence generation in this population: (1) Use clinical trials to improve the evidence base for treating older adults with cancer, (2) leverage research designs and infrastructure for generating evidence on older adults with cancer, (3) increase US Food and Drug Administration authority to incentivize and require research involving older adults with cancer, (4) increase clinicians' recruitment of older adults with cancer to clinical trials, and (5) use journal policies to improve researchers' reporting on the age distribution and health risk profiles of research participants.