Loss in life expectancy and gain in life years as measures of cancer impact |
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| Affiliation: | 1. Department of Health Sciences, University of Leicester, UK;2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden;3. Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden;1. Moffitt Cancer Center, Department of Health Outcomes and Behavior, 4115 E. Fowler Ave., Tampa, FL 33617, United States;2. Moffitt Cancer Center, Center for Immunization and Infection Research in Cancer, 12902 USF Magnolia Drive, Tampa, FL 33612, United States;3. Moffitt Cancer Center, Department of Cancer Epidemiology, 12902 USF Magnolia Drive, Tampa, FL 33612, United States;4. Moffitt Cancer Center, Department of Biostatistics and Bioinformatics, 12902 USF Magnolia Drive, Tampa, FL 33612, United States;5. University of South Florida, Department of Family Medicine, 13330 USF Laurel Drive, Tampa, FL 33612, United States;6. University of South Florida, Department of Epidemiology & Biostatistics, 13201 Bruce B Downs Blvd, Tampa, FL 33612, United States;8. University of Florida, Department of Medicine, 1600 SW Archer Rd., Gainesville, FL 32608, United States;9. University of Florida Health, Department of Health Outcomes and Biomedical Informatics, 2004 Mowry Road, Ste 2245, Gainesville, FL 32610, United States;10. University of Florida Health, Cancer Population Sciences, 2004 Mowry Road, Ste 2245, Gainesville, FL 32610, United States;1. Institute of Medical Epidemiology, Biostatistics and Informatics, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Germany;2. Section of Cancer Information, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France;3. Department of Medicine, University of Zimbabwe School of Medicine, Harare, Zimbabwe;4. Department of Pathology, Maputo Central Hospital, Maputo, Mozambique;5. CTSU, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7FL, United Kingdom;6. African Cancer Registry Network, 267 Banbury Road, Oxford OX2 7HT, United Kingdom;1. Universidad de Las Palmas de Gran Canaria, Calle Juan de Quesada 30, 35001 Las Palmas de Gran Canaria, Spain;2. Dermatology Department, Hospital Universitario de Gran Canaria Doctor Negrín, Barranco de la Ballena s/n, 35010 Las Palmas de Gran Canaria, Spain;3. Research Unit, Hospital Universitario de Gran Canaria Doctor Negrín, Barranco de la Ballena s/n, 35010 Las Palmas de Gran Canaria, Spain;1. National Cancer Registration and Analysis Service, Public Health England, Wellington House – 6th Floor, 133-155 Waterloo Rd, London, SE1 8UG, United Kingdom;2. Department of Breast Surgery, Bexley Cancer Centre, St James’s University Hospital, Beckett St, Leeds, LS9 7TF, United Kingdom;3. Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, United Kingdom;1. Department of Environment and Health, Istituto Superiore di Sanità, viale Regina Elena, 299 I-00161 Rome, Italy;2. Statistical Service, Istituto Superiore di Sanità, viale Regina Elena, 299 I-00161 Rome, Italy |
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| Abstract: | There are a broad range of survival-based metrics that are available to report from cancer survival studies, with varying advantages and disadvantages. A combination of metrics should be considered to improve comprehensibility and give a fuller understanding of the impact of cancer. In this article, we discuss the utility of loss in life expectancy and gain in life years as measures of cancer impact, and to quantify differences across population groups. These measures are simple to interpret, have a real-world meaning, and evaluate impact over a life-time horizon. We illustrate the use of the loss in life expectancy measures through a range of examples using data on women diagnosed with cancer in England. We use four different examples across a number of tumour types to illustrate different uses of the metrics, and highlight how they can be interpreted and used in practice in population-based oncology studies. Extensions of the measures conditional on survival to specific times after diagnosis can be used to give updated prognosis for cancer patients. Furthermore, we show how the measures can be used to understand the impact of population differences seen across patient groups. We believe that these under-used, and relatively easy to calculate, measures of overall impact can supplement reporting of cancer survival metrics and improve the comprehensibility compared to the metrics typically reported. |
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| Keywords: | Life expectancy Inequalities Cancer survival Population-based studies Statistical methods |
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