Prediagnostic plasma IGFBP-1, IGF-1 and risk of prostate cancer

Insulin-like growth factor (IGF)-1 is associated with a higher risk of prostate cancer. IGF-binding protein (IGFBP)-1, a marker for insulin activity, also binds IGF-1 and inhibits its action. Data on IGFBP-1 and prostate cancer risk are sparse and whether the IGF and insulin axes interact to affect prostate cancer carcinogenesis is unknown. We evaluated the independent and joint influence of prediagnostic plasma levels of IGFBP-1 (fasting) and IGF-1 on risk of prostate cancer among 957 cases and 1,021 controls with fasting levels of IGFBP-1 and 1,709 cases and 1,778 controls with IGF-1 nested within the Health Professionals Follow-up Study. Unconditional logistic regression adjusting for matching factors was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Higher prediagnostic fasting IGFBP-1 levels were associated with lower risk of prostate cancer (highest vs. lowest quartile OR = 0.67, 95% CI 0.52-0.86, ptrend  = 0.003), which remained similar after adjusting for IGF-1. Prediagnostic IGF-1 was associated with increased risk of prostate cancer (highest vs. lowest quartile OR = 1.28, 95% CI = 1.05-1.56, ptrend  = 0.01). The associations with each marker were primarily driven by lower-grade and non-advanced prostate cancer. Being low in IGFBP-1 and high in IGF-1 did not confer appreciable additional risk (pinteraction  = 0.42). In summary, prediagnostic fasting IGFBP-1 may influence prostate cancer carcinogenesis. Being low in IGFBP-1 or high in IGF-1 is sufficient to elevate the risk of prostate cancer

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants