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Patient Experience Captured by Quality-of-Life Measurement in Oncology Clinical Trials.

ABSTRACT: Importance:Quality of life (QoL) is an important consideration in cancer medicine, especially because drugs are becoming more costly and may only result in modest gains in overall survival. However, there has been no descriptive analysis for the points at which QoL is measured in cancer trials. Objective:To estimate the prevalence of studies that measure QoL at different points and see how many studies measure QoL for the entirety of a patient's life. Design, Setting, and Participants:This cross-sectional analysis includes all articles on oncology clinical trials in the 3 highest-impact oncology journals, published between July 2015 and June 2018, that reported QoL outcomes. Main Outcomes and Measures:Data were abstracted on when QoL was assessed and the characteristics of these studies. Results:For all 149 studies that met inclusion criteria, QoL assessment was high during treatment (104 articles [69.8%]), during follow-up (81 articles [54.4%]), and after the end of the intervention (68 articles [45.6%]). In 5 of the 149 studies (3.4%), QoL was assessed until death, including in only 1 of the 74 studies on metastatic or incurable cancers. Among these 5 studies, only 1 (20%) used a drug intervention, 1 (20%) used a behavioral intervention, and 2 (40%) used a radiation intervention; only 1 of 5 was in the metastatic setting. The number of studies that reported a positive QoL outcome (ie, QoL outcome was more favorable in the intervention group than in the control group) was between 42 of 81 articles (51.9%) and 16 of 28 articles (57.1%) for most QoL assessment points but only 1 of 5 articles (20%) for studies measuring QoL until death. Conclusions and Relevance:This study found that most clinical trials assessed QoL during the treatment or intervention and often during a given amount of follow-up but infrequently assessed QoL on disease progression and rarely followed QoL until the end of the patient's life. Most studies reporting QoL until the end of life reported worse QoL outcomes for the intervention group than the control group. Future research and policy recommendations should consider not just short-term QoL outcomes but QoL outcomes throughout the patient's cancer care.

SUBMITTER: Haslam A

PROVIDER: S-EPMC7057133 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Although there are discussions regarding standards of the analysis of patient-reported outcomes and quality of life (QOL) in oncology clinical trials, that of QOL with death events is not within their scope. For example, ignoring death can lead to bias in the QOL analysis for patients with moderate or high mortality rates in the palliative care setting. This is discussed in the estimand framework but is controversial. Information loss by summary measures under the estimand framework may make it challenging for clinicians to interpret the QOL analysis results. This study illustrated the use of graphical displays in the framework. They can be helpful for discussions between clinicians and statisticians and decision-making by stakeholders. We reviewed the time-to-deterioration analysis, prioritized composite outcome approach, semi-competing risk analysis, survivor analysis, linear mixed model for repeated measures, and principal stratification approach. We summarized attributes of estimands and graphs in the statistical analysis and evaluated them in various hypothetical randomized controlled trials. Graphs for each analysis method provide different information and impressions. In the time-to-deterioration analysis, it was not easy to interpret the difference in the curves as an effect on QOL. The prioritized composite outcome approach provided new insights for QOL considering death by defining better conditions based on the distinction of OS and QOL. The semi-competing risk analysis provided different insights compared with the time-to-deterioration analysis and prioritized composite outcome approach. Due to the missing assumption, graphs by the linear mixed model for repeated measures should be carefully interpreted, even for descriptive purposes. The principal stratification approach provided pure comparison, but the interpretation was difficult because the target population was unknown. Graphical displays can capture different aspects of treatment effects that should be described in the estimand framework.

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Patient Experience Captured by Quality-of-Life Measurement in Oncology Clinical Trials.

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