Effects of Recall and Selection Biases on Modeling Cancer Risk From Mobile Phone Use: Results From a Case-Control Simulation Study

Abstract

Background: The largest case-control study (Interphone study) investigating glioma risk related to mobile phone use showed a J-shaped relationship with reduced relative risks for moderate use and a 40% increased relative risk among the 10% heaviest regular mobile phone users, using a categorical risk model based on deciles of lifetime duration of use among ever regular users.

Methods: We conducted Monte Carlo simulations examining whether the reported estimates are compatible with an assumption of no effect of mobile phone use on glioma risk when the various forms of biases present in the Interphone study are accounted for. Four scenarios of sources of error in self-reported mobile phone use were considered, along with selection bias. Input parameters used for simulations were those obtained from Interphone validation studies on reporting accuracy and from using a nonresponse questionnaire.

Results: We found that the scenario simultaneously modeling systematic and random reporting errors produced a J-shaped relationship perfectly compatible with the observed relationship from the main Interphone study with a simulated spurious increased relative risk among heaviest users (odds ratio = 1.91) compared with never regular users. The main determinant for producing this J shape was higher reporting error variance in cases compared with controls, as observed in the validation studies. Selection bias contributed to the reduced risks as well.

Conclusions: Some uncertainty remains, but the evidence from the present simulation study shifts the overall assessment to making it less likely that heavy mobile phone use is causally related to an increased glioma risk.

Figure.

Boxplots (over 5000 replicates) of (log) risk estimates associated with deciles of exposure (total duration of calls) in the absence of an effect (H₀; OR* = 1.0) for different scenarios.^a True (without error) and naive (with error) estimators.^aIn all scenarios, nonregular mobile phone users served as the reference category. The true OR (OR*) used for generating the model is supposed to be equal to 1.0. Scenarios were: differential random and systematic scenario: cases have greater random (10% more) and average systematic (τ = 0.34) error than controls, with the error increases with the level of use (γ = 0.02) and the random standard deviation error is set to 1.22 among controls (${\mathrm{\sigma }}_{T_0}$) (D; scenario 1); differential random scenario; cases have greater random error than controls (average standard deviations ratio between cases and controls equal to 1.1) (B; scenario 2); differential systematic scenario: cases have greater average systematic error than controls (expectation τ = 0.34) with the error increased with the level of use (γ = 0.02) and the random error is kept at a constant level (of 1.28) and similar among cases and controls (C; scenario 3); random error scenario: the random standard deviation ${\mathrm{\sigma }}_T$ was set to 1.22 (A; scenario 4).