title: The impact of follow-up type and missed deaths on population-based cancer survival studies for Hispanics and Asians.
abstract: BACKGROUND
  The accuracy of cancer survival statistics relies on the quality of death linkages and follow-up information collected by population-based cancer registries. Methodological issues on survival data by race-ethnicity in the United States, in particular for Hispanics and Asians, have not been well studied and may undermine our understanding of survival disparities.
  
  
  METHODS
  Based on Surveillance, Epidemiology, and End Results (SEER)-18 data, we analyzed existing biases in survival statistics when comparing the four largest racial-ethnic groups in the United States, whites, blacks, Hispanics and Asians. We compared the "reported alive" method for calculation of survival, which is appropriate when date of last alive contact is available for all cases, with the "presumed alive" method used when dates of last contact are unavailable. Cox regression was applied to calculate the likelihood of incomplete follow-up (those with less than 5 years of vital status information) according to racial-ethnic group and stage of diagnosis. Finally, potentially missed deaths were estimated based on the numbers of cases with incomplete follow-up for highly fatal cancers.
  
  
  RESULTS
  The presumed alive method overestimated survival compared with the reported alive method by as much as 0.9-6.2 percentage points depending on the cancer site among Hispanics and by 0.4-2.7 percentage points among Asians. In SEER data, Hispanics and Asians are more likely to have incomplete follow-up than whites or blacks. The assumption of random censoring across race-ethnicity is not met, as among non-white cases, those who have a worse prognosis are more likely to have incomplete follow-up than those with a better prognosis (P < .05). Moreover, death ascertainment is not equal across racial-ethnic groups. Overall, 3% of cancer deaths were missed among Hispanics and Asians compared with less than 0.5% among blacks and whites.
  
  
  CONCLUSIONS
  Cancer survival studies involving Hispanics and Asians should be interpreted with caution because the current available data overtly inflates survival in these populations. Censoring is clearly nonrandom across race-ethnicity meaning that findings of Hispanic and Asian survival advantages may be biased. Problematic death linkages among Hispanics and Asians contribute to missing deaths and overestimated survival. More complete follow-up with at least 5 years of information on vital status as well as improved death linkages will decisively increase the validity of survival estimates for these growing populations.
authors.affiliation: Epidemiology and Biostatistics, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV (PSP, TJB), California Cancer Reporting and Epidemiologic Surveillance (CalCARES) Program, Institute for Public Health Improvement, University of California Davis Health System, Sacramento, CA (CRM); Los Angeles Cancer Surveillance Program, Keck School of Medicine, University of Southern California, Los Angeles, CA (LL); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD (SFA). paulo.pinheiro@unlv.edu.
authors.forename: Paulo S
authors.initials: PS
authors.lastname: Pinheiro
authors.affiliation: Epidemiology and Biostatistics, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV (PSP, TJB), California Cancer Reporting and Epidemiologic Surveillance (CalCARES) Program, Institute for Public Health Improvement, University of California Davis Health System, Sacramento, CA (CRM); Los Angeles Cancer Surveillance Program, Keck School of Medicine, University of Southern California, Los Angeles, CA (LL); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD (SFA).
authors.forename: Cyllene R
authors.initials: CR
authors.lastname: Morris
authors.affiliation: Epidemiology and Biostatistics, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV (PSP, TJB), California Cancer Reporting and Epidemiologic Surveillance (CalCARES) Program, Institute for Public Health Improvement, University of California Davis Health System, Sacramento, CA (CRM); Los Angeles Cancer Surveillance Program, Keck School of Medicine, University of Southern California, Los Angeles, CA (LL); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD (SFA).
authors.forename: Lihua
authors.initials: L
authors.lastname: Liu
authors.affiliation: Epidemiology and Biostatistics, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV (PSP, TJB), California Cancer Reporting and Epidemiologic Surveillance (CalCARES) Program, Institute for Public Health Improvement, University of California Davis Health System, Sacramento, CA (CRM); Los Angeles Cancer Surveillance Program, Keck School of Medicine, University of Southern California, Los Angeles, CA (LL); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD (SFA).
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Bungum
authors.affiliation: Epidemiology and Biostatistics, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV (PSP, TJB), California Cancer Reporting and Epidemiologic Surveillance (CalCARES) Program, Institute for Public Health Improvement, University of California Davis Health System, Sacramento, CA (CRM); Los Angeles Cancer Surveillance Program, Keck School of Medicine, University of Southern California, Los Angeles, CA (LL); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD (SFA).
authors.forename: Sean F
authors.initials: SF
authors.lastname: Altekruse
mesh_terms: D001213:Asian / Q000706:statistics & numerical data*; D003710:Demography; D005006:Ethnicity / Q000706:statistics & numerical data*; D005500:Follow-Up Studies; D006630:Hispanic or Latino / Q000706:statistics & numerical data*; D006801:Humans; D009369:Neoplasms / Q000401:mortality*; D016016:Proportional Hazards Models; D018426:SEER Program; D012959:Socioeconomic Factors; D015996:Survival Rate; D014481:United States