Selection bias in cancer risk estimation from A-bomb survivors

Radiat Res. 2007 Jun;167(6):735-41. doi: 10.1667/RR0349.1.

Abstract

We consider the possible bias in cancer risk estimation from A-bomb survivors due to selection of the cohort by survival. The paper considers both relevant information from the data and basic theoretical issues involved. The most direct information from the data comes from making various restrictions on the dose-distance range, partly to reduce differential selection and partly just to reduce the magnitude of the selection. These analyses suggest that there are no serious biases, but they are not conclusive. Theoretical considerations include laying out more explicitly than usual just how biases could result from the selection. This involves heterogeneities in the ability to survive acute effects, in baseline and radiogenic cancer rates, and most importantly the correlation between survival-related and cancer-related heterogeneities. Following on this, idealized modeling is used to quantify the extent of possible bias in terms of the assumed values of the magnitude of these heterogeneities and their correlation. It is indicated that these values would need to be very large to introduce substantial bias. Based on all these considerations, it seems unlikely that the bias in cancer risk estimation could be large in relation to other uncertainties in generalizing from what is seen among A-bomb survivors; in particular, indications are that the bias in relative risks is unlikely to be as large as 0.05 to 0.07. For solid cancer this would correspond to bias in the excess relative risk at 1 Sv of at most about 15-20%.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Bias
  • Epidemiologic Methods*
  • Humans
  • Incidence
  • Japan / epidemiology
  • Lung Neoplasms / epidemiology*
  • Neoplasms, Radiation-Induced / epidemiology*
  • Nuclear Warfare / statistics & numerical data*
  • Proportional Hazards Models*
  • Reproducibility of Results
  • Risk Assessment / methods*
  • Risk Factors
  • Sensitivity and Specificity
  • Survivors / statistics & numerical data*