Impact of comparison group on cohort dose response regression: an example using risk estimation in atomic-bomb survivors.
Cohort-based dose-response analyses can be biased if based on a comparison group that is not comparable to the exposed persons with respect to uncontrolled factors related to disease incidence or mortality. When data exist over a range of doses including the very low dose region, internal regression standardized analyses based on the regression intercept derived from the exposed subcohort alone can provide risk estimates that are not subject to such comparison-group bias. In the Life Span Study cohort of atomic-bomb survivors, persons with dose estimates of zero comprise a broader geographic distribution than that of persons with non-zero dose estimates. Because there is geographic variation in mortality rates, the zero-dose persons might bias background rate estimates thereby affecting inference about radiation risk. This is illustrated using mortality due to all causes. Restricting the comparison group to certain geographically defined subcohorts resulted in as much as a 6% increase or 8% decrease in the risk estimate. This bias can be corrected using an SMR-type estimate in the regression model, allowing retention of the comparison group in the analysis if it is needed for stability or precision in estimating age, time, and sex effects. Consideration of heterogeneity in comparison groups is particularly important in dose-response studies focused on low doses at which the response may be comparable in magnitude to such heterogeneity.
Cologne
J B
JB
Department of Statistics, Hiroshima, Japan. cologne@rerf.or.jp
Preston
D L
DL
D000367:Age Factors; D015982:Bias; D015331:Cohort Studies; D004307:Dose-Response Relationship, Radiation; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007564:Japan; D008017:Life Expectancy; D008297:Male; D009026:Mortality; D009689:Nuclear Warfare; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods; D012737:Sex Factors; D017741:Survivors / Q000706:statistics & numerical data*; D013997:Time Factors