Effects of recall and selection biases on modeling cancer risk from mobile phone use: Results from a

Abstract

Effects of recall and selection biases on modeling cancer risk from mobile phone use: Results from a case-control simulation study (My note: The models tested in this simulation study were based on questionable assumptions.) Bouaoun L, Byrnes G, Lagorio S, Feychting M, Abou-Bakre A, Beranger R, Schüz J. Effects of recall and selection biases on modeling cancer risk from mobile phone use: Results from a case-control simulation study. Epidemiology. 2024 May 20. doi: 10.1097/EDE.0000000000001749. 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 non-response 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 (OR = 1.91) compared to never regular users. The main determinant for producing this J shape was higher reporting error variance in cases compared to 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. Open access paper: pubmed.ncbi.nlm.nih.gov

AI evidence extraction

Main findings

Monte-Carlo simulations incorporating reporting errors and selection bias produced a J-shaped exposure–response pattern compatible with the Interphone study results under an assumption of no causal effect. The simulated spurious increased risk among the heaviest users was OR=1.91 versus never regular users, with higher reporting error variance in cases than controls as a key driver; selection bias contributed to reduced risks.

Outcomes measured

• glioma risk
• cancer risk modeling (glioma)

Limitations

• Simulation study; results depend on modeled assumptions and input parameters.
• Only biases/errors described (self-report reporting errors and selection bias) were modeled; other potential sources of bias are not described in the abstract.
• No frequency, SAR, or detailed exposure metrics reported in the abstract.

Suggested hubs

• mobile-phones-cancer (0.9)
  Focuses on modeling glioma risk in relation to mobile phone use (Interphone).

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    "study_type": "other",
    "exposure": {
        "band": "RF",
        "source": "mobile phone",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": null,
    "sample_size": null,
    "outcomes": [
        "glioma risk",
        "cancer risk modeling (glioma)"
    ],
    "main_findings": "Monte-Carlo simulations incorporating reporting errors and selection bias produced a J-shaped exposure–response pattern compatible with the Interphone study results under an assumption of no causal effect. The simulated spurious increased risk among the heaviest users was OR=1.91 versus never regular users, with higher reporting error variance in cases than controls as a key driver; selection bias contributed to reduced risks.",
    "effect_direction": "no_effect",
    "limitations": [
        "Simulation study; results depend on modeled assumptions and input parameters.",
        "Only biases/errors described (self-report reporting errors and selection bias) were modeled; other potential sources of bias are not described in the abstract.",
        "No frequency, SAR, or detailed exposure metrics reported in the abstract."
    ],
    "evidence_strength": "low",
    "confidence": 0.7399999999999999911182158029987476766109466552734375,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "mobile phone",
        "RF exposure",
        "glioma",
        "Interphone",
        "case-control",
        "Monte-Carlo simulation",
        "recall bias",
        "selection bias",
        "reporting error",
        "exposure misclassification"
    ],
    "suggested_hubs": [
        {
            "slug": "mobile-phones-cancer",
            "weight": 0.90000000000000002220446049250313080847263336181640625,
            "reason": "Focuses on modeling glioma risk in relation to mobile phone use (Interphone)."
        }
    ]
}

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