Hanne C Lie1,2,3, Corina S Rueegg4, Sophie D Fosså5, Jon H Loge6,7, Ellen Ruud8,9, Cecilie E Kiserud5. 1. Department of Paediatric Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway. h.c.lie@medisin.uio.no. 2. Department of Behavioural Sciences in Medicine, Institute of Basic Medical Sciences in Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. h.c.lie@medisin.uio.no. 3. National Advisory Unit on Late Effects after Cancer Treatment, Oslo University Hospital, Radiumhospitalet, PO 4953, Nydalen, NO-0424, Oslo, Norway. h.c.lie@medisin.uio.no. 4. Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital and Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway. 5. National Advisory Unit on Late Effects after Cancer Treatment, Oslo University Hospital, Radiumhospitalet, PO 4953, Nydalen, NO-0424, Oslo, Norway. 6. Department of Behavioural Sciences in Medicine, Institute of Basic Medical Sciences in Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. 7. Regional Advisory Unit in Palliative Care, Department of Oncology, Oslo University Hospital, Oslo, Norway. 8. Department of Paediatric Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway. 9. Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Abstract
PURPOSE: Declining response rates threaten the generalizability of health surveys. We investigate (1) the effect of item order on response rate; (2) characteristics of early , late and non-responders; and (3) potential non-response bias in a population-based health survey of childhood, adolescent and young adult cancer survivors (CAYACS). METHODS: We mailed a questionnaire survey to 5361 eligible CAYACS identified by the Cancer Registry of Norway (CRN), representing a range of cancer diagnoses. The 302-item questionnaire included a range of survivorship-related questions and validated patient-reported outcome measures. To investigate item-order effects on response rates, we constructed two versions of the questionnaire presenting cancer-related or socio-demographic items first. The CRN provided demographic and clinical information for the total population. Risk of non-response bias was estimated by (1) comparing outcomes between early and late responders (answered after a reminder), and (2) by applying inverse probability of participation weights to construct a total population (with 100% response) and then compare 21 a priori selected outcomes between early responders, all responders (early + late) and the total population (all eligible). RESULTS: Survey item order did not affect response rates (cancer first 49.8% vs socio-demographic first 50.2%). Shorter time since diagnosis, male gender and a malignant melanoma diagnosis remained significant predictors of non-response in a multivariable multinomial regression model. There were no significant differences on 16/21 survey outcomes between early and late responders, and 18/21 survey outcomes between early responders, all responders and the total population. CONCLUSION: Despite a modest response rate, we found little evidence for a response bias in our study. IMPLICATIONS FOR CANCER SURVIVORS: Surveys of survivor-reported outcomes with low response rates may still be valuable and generalizable to the total survivor population.
PURPOSE: Declining response rates threaten the generalizability of health surveys. We investigate (1) the effect of item order on response rate; (2) characteristics of early , late and non-responders; and (3) potential non-response bias in a population-based health survey of childhood, adolescent and young adult cancer survivors (CAYACS). METHODS: We mailed a questionnaire survey to 5361 eligible CAYACS identified by the Cancer Registry of Norway (CRN), representing a range of cancer diagnoses. The 302-item questionnaire included a range of survivorship-related questions and validated patient-reported outcome measures. To investigate item-order effects on response rates, we constructed two versions of the questionnaire presenting cancer-related or socio-demographic items first. The CRN provided demographic and clinical information for the total population. Risk of non-response bias was estimated by (1) comparing outcomes between early and late responders (answered after a reminder), and (2) by applying inverse probability of participation weights to construct a total population (with 100% response) and then compare 21 a priori selected outcomes between early responders, all responders (early + late) and the total population (all eligible). RESULTS: Survey item order did not affect response rates (cancer first 49.8% vs socio-demographic first 50.2%). Shorter time since diagnosis, male gender and a malignant melanoma diagnosis remained significant predictors of non-response in a multivariable multinomial regression model. There were no significant differences on 16/21 survey outcomes between early and late responders, and 18/21 survey outcomes between early responders, all responders and the total population. CONCLUSION: Despite a modest response rate, we found little evidence for a response bias in our study. IMPLICATIONS FOR CANCER SURVIVORS: Surveys of survivor-reported outcomes with low response rates may still be valuable and generalizable to the total survivor population.
Entities:
Keywords:
Childhood cancer survivors; Health survey; Non-response bias; Response rate
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