Rebecca L Mercieca-Bebber1,2, Melanie A Price2,3, Melanie L Bell2,4, Madeleine T King1,2, Penelope M Webb5, Phyllis N Butow2,3. 1. Central Clinical School, Sydney Medical School, University of Sydney, NSW, Australia. 2. Psycho-oncology Co-operative Research Group (PoCoG), School of Psychology, University of Sydney, NSW, Australia. 3. Centre for Medical Psychology and Evidence-Based Decision-Making (CeMPeD), School of Psychology, University of Sydney, NSW, Australia. 4. Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA. 5. QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Abstract
AIMS: Participant drop out is a major barrier to high-quality patient-reported outcome (PRO) data analysis in cancer research as patients with worsening health are more likely to dropout. To test the hypothesis that ovarian cancer patients with worse PROs would drop out earlier, we examined how patients differed by time of dropout on health-related quality of life (HRQOL), anxiety, depression, optimism and insomnia. METHODS: This analysis included 619 participants, stratified by time of dropout, from the Australian Ovarian Cancer Study - Quality of Life substudy, in which participants completed PRO questionnaires at three-monthly intervals for 21 months. Trends in PROs over time were examined. Pearson correlations examined the relationship between time of dropout and baseline PROs. Multiple linear regression models including age, disease stage and time since diagnosis examined relationships between baseline and final PRO scores, and final PRO scores and dropout group. RESULTS: Participants who dropped out earlier had significantly worse baseline HRQOL (p < 0.0001) and higher depression (p < 0.0001). For all five PROs, final scores were significantly associated with baseline scores (p < 0.0001). Time of dropout was significantly associated with final HRQOL (p = 0.003), anxiety (p = 0.05), depression (p = 0.02) and optimism (p = 0.02) scores. Depression, HRQOL and anxiety worsened at a faster rate overtime in dropouts than study completers. CONCLUSIONS: Poorer HRQOL and higher depression at baseline, and final HRQOL, anxiety, depression and optimism scores were predictive of time of dropout. These results highlight the importance of collecting auxiliary data to inform careful and considered handling of missing PRO data during analysis, interpretation and reporting.
AIMS: Participant drop out is a major barrier to high-quality patient-reported outcome (PRO) data analysis in cancer research as patients with worsening health are more likely to dropout. To test the hypothesis that ovarian cancerpatients with worse PROs would drop out earlier, we examined how patients differed by time of dropout on health-related quality of life (HRQOL), anxiety, depression, optimism and insomnia. METHODS: This analysis included 619 participants, stratified by time of dropout, from the Australian Ovarian Cancer Study - Quality of Life substudy, in which participants completed PRO questionnaires at three-monthly intervals for 21 months. Trends in PROs over time were examined. Pearson correlations examined the relationship between time of dropout and baseline PROs. Multiple linear regression models including age, disease stage and time since diagnosis examined relationships between baseline and final PRO scores, and final PRO scores and dropout group. RESULTS:Participants who dropped out earlier had significantly worse baseline HRQOL (p < 0.0001) and higher depression (p < 0.0001). For all five PROs, final scores were significantly associated with baseline scores (p < 0.0001). Time of dropout was significantly associated with final HRQOL (p = 0.003), anxiety (p = 0.05), depression (p = 0.02) and optimism (p = 0.02) scores. Depression, HRQOL and anxiety worsened at a faster rate overtime in dropouts than study completers. CONCLUSIONS: Poorer HRQOL and higher depression at baseline, and final HRQOL, anxiety, depression and optimism scores were predictive of time of dropout. These results highlight the importance of collecting auxiliary data to inform careful and considered handling of missing PRO data during analysis, interpretation and reporting.
Authors: Claudia Rutherford; Manish I Patel; Margaret-Ann Tait; David P Smith; Daniel S J Costa; Shomik Sengupta; Madeleine T King Journal: Qual Life Res Date: 2020-09-22 Impact factor: 4.147
Authors: Imogen Ramsey; Belle H de Rooij; Floortje Mols; Nadia Corsini; Nicole J E Horevoorts; Marion Eckert; Lonneke V van de Poll-Franse Journal: J Cancer Surviv Date: 2019-09-06 Impact factor: 4.442
Authors: Lene Kongsgaard Nielsen; Madeleine King; Sören Möller; Mary Jarden; Christen Lykkegaard Andersen; Henrik Frederiksen; Henrik Gregersen; Anja Klostergaard; Morten Saaby Steffensen; Per Trøllund Pedersen; Maja Hinge; Mikael Frederiksen; Bo Amdi Jensen; Carsten Helleberg; Anne Kærsgaard Mylin; Niels Abildgaard Journal: Qual Life Res Date: 2019-09-23 Impact factor: 4.147