Meta-analyses of cancer risk among low-dose radiation studies must consider sources and magnitude of bias within each contributing study. Each study possesses limitations in methods and available data that may contribute to bias. The major sources of bias for leukaemia risk in low-dose observational studies are (1) dose error and uncertainty and (2) exposure to other leukaemogens that is differentially distributed with respect to ionising radiation. With regard to the former, most studies of nuclear workers involve individual badge dosimetry, the magnitude of which has been quantified in recent studies.53
These errors have generally been found to be small ‘classical’ errors which would be more likely to cause bias toward the null.54
Environmental studies, by contrast, frequently involve exposures that are Berksonian in error and can have uncertain effects on risk estimates.54
Regarding the latter source, exposures to benzene, 1,3-butadiene, carbon tetrachloride, ethylene oxide, formaldehyde, and trichloroethylene are known (ie, benzene) or suspected to be linked to leukaemia.55 56
Of these chemicals, benzene is the only known leukaemogen likely to be associated with radiation exposures, and then only among worker-based studies. A previous pooled study found that inclusion of benzene slightly reduced radiation–leukaemia risk estimates.23
The risk estimate for this study (within this meta-analysis) is benzene-adjusted. Other worker-based studies, particularly those based on more recent employment, have found little evidence of potential benzene exposures, as this substance was never used or has been phased out of many workplaces.29 40
Unlike solid cancers, leukaemia is advantageous for meta-analysis because it is not strongly associated with lifestyle factors, although there is some evidence of a moderate association (RR=1.4–2.0) between smoking and some leukaemias.57–59
Given the small magnitude of the observed association, occupational radiation exposure and smoking would need to be highly correlated to account for even a modest effect on the dose–response trend for leukaemia.60
Nonetheless, the effects of smoking and concomitant leukaemogen exposures are largely unaccounted for in most studies, which is a limitation in our meta-analysis.52
Studies suitable for inclusion were limited to populations primarily exposed to low-LET radiations; however, concomitant exposures to neutrons and internally deposited alpha-emitters were likely in many situations. Some studies included high-LET exposures so that the effects due solely to low-LET exposures could not be examined.27 29
Thus, there is a potential for bias from limitations in adjusting for dose heterogeneity. However, recent studies of the relationship between low-LET irradiation and leukaemia have shown little effect from adjusting for plutonium exposures 20 23
and have also shown exposures to neutrons and alpha-emitting radionuclides are typically small relative to low-LET exposures.23 24 27 29 32–34 37 38 42
Therefore, a substantial bias from dose heterogeneity in selected studies is unlikely.
Three primary studies (13%) reported results from log-linear dose–response modelling.19 24 38
We assumed that risk estimates from log-linear and linear models using these data would be similar at low doses (ie, <100 mGy) because dose ranges did not appear great. However, the uncertainty between these models may substantially differ. Our sensitivity analyses revealed little evidence that the log-linear model results had a strong effect on the aggregate estimates as shown by models If and IIf. Thus, our estimates of ERR at 100 mGy as a risk per 100 mGy may be reasonable at exposures below this level, but caution should be used when extrapolating above this level.
Obtaining sufficient information to estimate study precision was problematic. It was common for studies to report uncertainty using confidence intervals calculated from profile likelihood methods, and the distribution of the likelihood function was unknown. Reported confidence intervals were often asymmetric and were sometimes reported as inestimable due to parameter space constraints of the model. We were able to obtain the original estimates of standard errors for seven studies. Comparing our estimates to reported standard errors suggested that our methods slightly overestimated standard errors. Replacing our estimates with reported values or adjusting to account for an observed bias did not noticeably change the aggregate estimate (data not shown). Thus, there is little evidence of a significant bias due to inadequate estimation of the standard errors. However, more work is needed to examine the coverage of confidence intervals for the aggregate estimates.
Although we observed little change in estimates following adjustment, there was evidence of publication bias away from the null. It is likely that smaller and earlier studies lacked the statistical power needed to perform informative dose–response analyses. Of 15 studies that were superseded by studies with improved follow-up, seven (47%) reported negative ERR values. Moreover, some larger studies included dose–response modelling only if an elevation was observed in other comparisons (eg, standardised mortality ratio (SMR), standardised rate ratio (SRR)).61
Our analysis suggests that model II results were the least affected by publication bias. We propose this bias was minimised because the large pooled studies used in model II included many small cohorts that likely had null or negative values, which may not have been published individually with dose–response estimates. We reported results following an adjustment for publication bias in some models, although publication bias cannot be confirmed without uncovering the actual ‘negative’ unpublished studies. We advise cautious interpretation of tests of heterogeneity and bias in our meta-analysis as there is no statistical test that can confirm or exclude bias with certainty.
We excluded CLL from our analysis because information on CLL risks was sparse among the studies examined. However, we note that CLL radiogenicity is the subject of recent enquiry.62–66
The majority of information obtained for our analysis resulted from mortality studies that may underestimate risks due to improved survival in leukaemia patients. Furthermore, several studies acknowledged difficulty in case ascertainment, most notably among incident and environmental studies. Thus, a potential bias may exist from underascertainment of incidence cases.