Missing quality of life data in cancer clinical trials: serious problems and challenges

Abstract

Measurement of quality of life (QOL) in cancer clinical trials has increased in recent years as more groups realize the importance of such endpoints. A key problem has been missing data. Some QOL data may unavoidably be missing, as for example when patients are too ill to complete forms. Other important sources are potentially avoidable and can broadly be divided into three categories: (i) methodological factors; (ii) logistic and administrative factors; (iii) patient-related factors. Logistic and administrative factors, for example, staff oversights, have proven to be most important. Since most QOL measurements require patient self-report, it is usually not possible to rectify the failure to collect baseline data or any follow-up assessments. There is strong evidence that such data are not 'missing at random', and cannot be ignored without introducing bias. Although several approaches to the analysis of partly missing data have been described, none is entirely satisfactory. Prevention of avoidable missing data is better than attempted cure. In July 1996, an international conference on missing QOL data in cancer clinical trials reported the experience of most major groups involved. This paper will serve as an introduction to the problem and provide an estimation of its magnitude, and approaches to its prevention and solution.

Quality of life as a clinical trial endpoint: determining the appropriate interval for repeated assessments in patients with advanced lung cancer

“…In most studies in which both survival and QOL is determined, the latter is a secondary endpoint. Experienced clinicians and data managers in collecting primary endpoints of survival, tumor response, and toxicity, may view QOL as a subsidiary outcome measure [1]. Both the power analysis for determining sample size and other considerations have been based on the survival endpoint.…”

“…The international workshop for "Missing Data in Quality of Life Research in Cancer Clinical Trials: Practical and Methodological Issues" reported that trials are often designed with the QOL interval of 3 weeks while on treatment and annually for 5 years [1]. But the design problem for the QOL assessment interval in clinical trials is illustrated by examples from a retrospective review of studies over the last 10 years in which permission to use the Lung Cancer Symptom Scale (LCSS) was requested.…”

“…Both investigators and patients may lose interest in QOL assessment when it is performed at intervals too often to be perceived as being relevant. Problems arise in serial measurement when observations are not missing at random [17,1,7,10,34]. In a 673-patient data set from two combined trials from 30 cancer centers for patients with advanced non-small cell lung cancer (NSCLC) [6,9], the loss of patients from evaluation was not random and correlated highly with the baseline patient-determined symptom burden and QOL [17].…”

“…Progression of disease or death were the main reasons for patient dropout from this QOL evaluation. Missing data is a serious problem for QOL evaluation, because the reason for loss is not generally independent of the condition of interest producing a measurement bias [1,17,29,33 ]. Internal validity is compromised if attrition is different among the comparison groups due to the effect of the treatment [2].…”

“…Many statistical methods have been suggested for the problem of missing observations using QOL as an endpoint, including imputation [34]. Nonetheless, a better strategy is to plan at the outset to avoid loss of data [17,1,7,10,30].…”