Assessment of lead-time bias in estimates of relative survival for breast cancer |
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| Affiliation: | 1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden;2. Department of Health Sciences, University of Leicester, UK;1. Department of Epidemiology, Fielding School of Public Health, University of California, CA, USA;2. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA;3. Division of Pediatric Hematology/Oncology, Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA;1. College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331;2. Birth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, TX 78714;1. Cancer Prevention Institute of California, Fremont, CA, United States;2. Department of Health Research and Policy (Epidemiology), Stanford University School of Medicine, Stanford, CA, United States;3. Department of Internal Medicine, Division of Hematology and Oncology, University of California Davis School of Medicine, Sacramento, CA, United States;1. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA;2. Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA;1. University of Tampere, School of Health Sciences, Tampere, Finland;2. Department of Clinical Chemistry, Helsinki University Central Hospital, Finland;3. Tampere University Hospital, Department of Urology and University of Tampere, Medical School, Tampere, Finland;4. Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands;5. Sahlgrenska Academy at Göteborg University, Gothenburg, Sweden;6. Memorial Sloan-Kettering Cancer Centre, Department of Surgery and Department of Epidemiology and Biostatistics, New York, NY, USA;7. Finnish Cancer Registry, Helsinki, Finland |
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| Abstract: | Relative survival ratios (RSRs) can be useful for evaluating the impact of changes in cancer care on the prognosis of cancer patients or for comparing the prognosis for different subgroups of patients, but their use is problematic for cancer sites where screening has been introduced due to the potential of lead-time bias. Lead-time is survival time that is added to a patient's survival time because of an earlier diagnosis irrespective of a possibly postponed time of death. In the presence of screening it is difficult to disentangle how much of an observed improvement in survival is real and how much is due to lead-time bias. Even so, RSRs are often presented for breast cancer, a site where screening has led to early diagnosis, with the assumption that the lead-time bias is small. We describe a simulation-based framework for studying the lead-time bias due to mammography screening on RSRs of breast cancer based on a natural history model developed in a Swedish setting. We have performed simulations, using this framework, under different assumptions for screening sensitivity and breast cancer survival with the aim of estimating the lead-time bias. Screening every second year among ages 40–75 was introduced assuming that screening had no effect on survival, except for lead-time bias. Relative survival was estimated both with and without screening to enable quantification of the lead-time bias. Scenarios with low, moderate and high breast cancer survival, and low, moderate and high screening sensitivity were simulated, and the lead-time bias assessed in all scenarios. |
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| Keywords: | Lead time Relative survival Breast cancer Mammography screening Simulation study |
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